[
    {
        "quest": "What class does the Naive Bayes classifier predict for a given observation?",
        "answers": [
            {
                "text": "The class maximizing the joint predictors probability",
                "image": ""
            },
            {
                "text": "The class minimizing the joint predictors probability",
                "image": ""
            },
            {
                "text": "The class maximizing the joint predictors/labels probability",
                "image": ""
            },
            {
                "text": "The class minimizing the joint predictors/labels probability",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "If your dataset has two variables \ud835\udc99, \ud835\udc99\u2032 such that \ud835\udc99 = \ud835\udc82 \u22c5 \ud835\udc99\u2032 for some constant a > 0, then you have:",
        "answers": [
            {
                "text": "overfitting",
                "image": ""
            },
            {
                "text": "underfitting",
                "image": ""
            },
            {
                "text": "multicollinearity",
                "image": ""
            },
            {
                "text": "supercollinearity",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "A na\u00efve Bayes classifier can deal with previously unseen feature-label combination through:",
        "answers": [
            {
                "text": "Laplacian smoothing",
                "image": ""
            },
            {
                "text": "Bootstrapping",
                "image": ""
            },
            {
                "text": "Stratified cross-validation",
                "image": ""
            },
            {
                "text": "Repeated sampling",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "For a linear regression model, the expected squared error can be decomposed in:",
        "answers": [
            {
                "text": "Variance and covariance",
                "image": ""
            },
            {
                "text": "SSE and SST",
                "image": ""
            },
            {
                "text": "Underfit and overfit",
                "image": ""
            },
            {
                "text": "Bias and variance noise",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": " What is the key assumption of the Na\u00efve Bayes Classifier?",
        "answers": [
            {
                "text": "The predictors and labels are independent",
                "image": ""
            },
            {
                "text": "Each predictor follows a Gaussian distribution",
                "image": ""
            },
            {
                "text": "The predictors are independent conditionally on the label",
                "image": ""
            },
            {
                "text": "The number of predictors is at most poly(n)",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Which one of the following performance indicates the best model for prediction?",
        "answers": [
            {
                "text": "\ud835\udc45' = 0.2 on training, \ud835\udc45' = 0.1 on test",
                "image": ""
            },
            {
                "text": "\ud835\udc45' = 0.7 on training, \ud835\udc45' = 0.7 on test",
                "image": ""
            },
            {
                "text": "\ud835\udc45' = 0.8 on training, \ud835\udc45' = 0.1 on test",
                "image": ""
            },
            {
                "text": "\ud835\udc45' = 0.9 on training, \ud835\udc45' = \u22120.9 on test",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "You want to predict the market price of a team\u2019s merchandising (t-shirts, hats..), according to the team\u2019s seasonal performance. You suggest using:",
        "answers": [
            {
                "text": "Linear regression",
                "image": ""
            },
            {
                "text": "Logistic regression",
                "image": ""
            },
            {
                "text": "Linear programming",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "When is the accuracy a misleading classifier performance measure?",
        "answers": [
            {
                "text": "When the population label proportions are unbalanced",
                "image": ""
            },
            {
                "text": "When the population label proportions are balanced",
                "image": ""
            },
            {
                "text": "When the sensitivity is high",
                "image": ""
            },
            {
                "text": "When the specificity is low",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "The goal of linear regression is to?",
        "answers": [
            {
                "text": "Make America great again",
                "image": ""
            },
            {
                "text": "Group similar observations together",
                "image": ""
            },
            {
                "text": "Learn a linear function from data",
                "image": ""
            },
            {
                "text": "Evaluate the amount of noise in the data",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "In the bias-variance decomposition of the expected squared error, what does high bias suggest?",
        "answers": [
            {
                "text": "Noisy data",
                "image": ""
            },
            {
                "text": "Overfitting",
                "image": ""
            },
            {
                "text": "Underfitting",
                "image": ""
            },
            {
                "text": "Crossfitting",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Social network users often form communities according to their tastes. If you had access to their personal data, you may verify this intuition by:",
        "answers": [
            {
                "text": "Linear Regression",
                "image": ""
            },
            {
                "text": "Logistic Regression",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            },
            {
                "text": "Linear programming",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "R^2 is a measure of:",
        "answers": [
            {
                "text": "Reliability of predictions",
                "image": ""
            },
            {
                "text": "Goodness of fit",
                "image": ""
            },
            {
                "text": "Significance of estimates",
                "image": ""
            },
            {
                "text": "Model complexity",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "A company wants to relate the monthly revenue to productivity parameters such as total number of working hours, etc. They could use:",
        "answers": [
            {
                "text": "Linear regression",
                "image": ""
            },
            {
                "text": "Logistic regression",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            },
            {
                "text": "Linear programming",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "How do you perform a linear regression in R?",
        "answers": [
            {
                "text": "lm(y ~ x, data)",
                "image": ""
            },
            {
                "text": "lm(y ~ x, data, family = \u201cbinomial\u201d)",
                "image": ""
            },
            {
                "text": "predict(y ~ x, data)",
                "image": ""
            },
            {
                "text": "predict(y ~ x, data, binomial)",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "Your friend proposes to cluster 300 observations by trying all possible clustering and taking the one that minimizes intra cluster variance. You observe that:",
        "answers": [
            {
                "text": "This is the only possible approach",
                "image": ""
            },
            {
                "text": "This does not produce a good clustering",
                "image": ""
            },
            {
                "text": "This does require a few seconds",
                "image": ""
            },
            {
                "text": "This does require a centuries",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Single-linkage clustering works by",
        "answers": [
            {
                "text": "Repeatedly recomputing the centroids of clusters",
                "image": ""
            },
            {
                "text": "Repeatedly merging smaller clusters into larger ones",
                "image": ""
            },
            {
                "text": "Enumerating all possible clustering of the given points",
                "image": ""
            },
            {
                "text": "Enumerating all possible points in a cluster",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "In linear regression, a high value of \ud835\udc79\ud835\udfd0 on the training set suggests:",
        "answers": [
            {
                "text": "A small error of the model on the fitted data",
                "image": ""
            },
            {
                "text": "A small error of the model on future predictions",
                "image": ""
            },
            {
                "text": "A large error of the model on the fitted data",
                "image": ""
            },
            {
                "text": "A large error of the model on future predictions",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "A logistic regression gives the following scores, preceded by the actual label: (Y, 0.85), (Y, 0.75), (N,0.6), (Y,0.5), (N, 0.4), (N, 0.2). For a sensitivity of at least 2/3, the best choice is to predict Y when the score is at least:",
        "answers": [
            {
                "text": "0.9",
                "image": ""
            },
            {
                "text": "0.75",
                "image": ""
            },
            {
                "text": "0.6",
                "image": ""
            },
            {
                "text": "0.45",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "Look at the confusion matrix below. What we can say?",
        "answers": [
            {
                "text": "The sensitivity is < 0.80%",
                "image": ""
            },
            {
                "text": "There are less positives than negatives",
                "image": ""
            },
            {
                "text": "The accuracy is > 90%",
                "image": ""
            },
            {
                "text": "The classifier predicts 1 on 60% of the times",
                "image": ""
            }
        ],
        "correct": 3,
        "image": "[encoded data, 16800 characters]"
    },
    {
        "quest": "A set of observations (\ud835\udc99\ud835\udfcf, \ud835\udc9a\ud835\udfcf), (\ud835\udc99\ud835\udfd0,\ud835\udc9a\ud835\udfd0)\u2026(\ud835\udc99\ud835\udc8f, \ud835\udc9a\ud835\udc8f) obeys the law \ud835\udc9a\ud835\udc8a \u2254 \ud835\udc82\ud835\udc99\ud835\udc8a + \ud835\udc83 + \ud835\udf3a\ud835\udc8a, where \ud835\udf3a\ud835\udc8a is some random noise. The task of estimating a and b from the dataset is called:",
        "answers": [
            {
                "text": "Logistic regression",
                "image": ""
            },
            {
                "text": "Linear regression",
                "image": ""
            },
            {
                "text": "Linear programming",
                "image": ""
            },
            {
                "text": "Logistic programming",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "Laplacian smoothing aims at:",
        "answers": [
            {
                "text": "Producing readable plots by using an average window",
                "image": ""
            },
            {
                "text": "Reducing the model\u2019s dependence on the noise",
                "image": ""
            },
            {
                "text": "Improving the feature quality by removing outliers",
                "image": ""
            },
            {
                "text": "Avoid penalizing previously unseen observations",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "A dataset of points (\ud835\udc99\ud835\udfcf, \ud835\udc9a\ud835\udfcf), (\ud835\udc99\ud835\udfd0,\ud835\udc9a\ud835\udfd0)\u2026(\ud835\udc99\ud835\udc8f, \ud835\udc9a\ud835\udc8f) has been generated by the model \ud835\udc9a\ud835\udc8a \u2254 \ud835\udc82\ud835\udc99\ud835\udc8a + \ud835\udc83 + \ud835\udf3a\ud835\udc8a, where \ud835\udf3a\ud835\udc8a is gaussian noise. Linear regression aims at estimating:",
        "answers": [
            {
                "text": "a and b",
                "image": ""
            },
            {
                "text": "a and \ud835\udf00",
                "image": ""
            },
            {
                "text": "x and b",
                "image": ""
            },
            {
                "text": "x and y",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "Which one of the following R commands selects only the rows of data where X equals 0?",
        "answers": [
            {
                "text": "select(data, X == 0)",
                "image": ""
            },
            {
                "text": "filter(data, X==0)",
                "image": ""
            },
            {
                "text": "summarize(data, X==0)",
                "image": ""
            },
            {
                "text": "table(data, X == 0)",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "If an algorithm has exponential complexity, then we can assume that:",
        "answers": [
            {
                "text": "In practice it is still fast enough to be useful",
                "image": ""
            },
            {
                "text": "It admits a polynomial-time algorithm",
                "image": ""
            },
            {
                "text": "It can be solved by finding an optimal clustering",
                "image": ""
            },
            {
                "text": "No technological progress will ever make it practical",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "If you have n points, what is the number of clusters that minimizes the within-cluster sum of square?",
        "answers": [
            {
                "text": "1",
                "image": ""
            },
            {
                "text": "k",
                "image": ""
            },
            {
                "text": "n",
                "image": ""
            },
            {
                "text": "We cannot say",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Which regression model has smaller squared error in fitting a real function \ud835\udc87(\ud835\udc99)?",
        "answers": [
            {
                "text": "A simple linear regression",
                "image": ""
            },
            {
                "text": "A logistic regression",
                "image": ""
            },
            {
                "text": "A polynomial regression of degree 2",
                "image": ""
            },
            {
                "text": "A polynomial regression of degree 10",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "A doping screening is tested on a pool of 800 athletes of which 796 are clean. The test is correct in 99% of the cases. What can we say about it?",
        "answers": [
            {
                "text": "It may have missed all of the doped athletes",
                "image": ""
            },
            {
                "text": "It may have missed all of the clean athletes",
                "image": ""
            },
            {
                "text": "It identified all of the doped athletes",
                "image": ""
            },
            {
                "text": "It identified all of the clean atheletes",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "In linear programming, the space of feasible solution is:",
        "answers": [
            {
                "text": "An arbitrary set",
                "image": ""
            },
            {
                "text": "A subset of \ud835\udc45'",
                "image": ""
            },
            {
                "text": "A convex polytope",
                "image": ""
            },
            {
                "text": "None of the above",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "The explained variance of a clustering equals:",
        "answers": [
            {
                "text": "Within-cluster SSE divided by total sum of squares",
                "image": ""
            },
            {
                "text": "Total sum of squares divided by within-cluster SSE",
                "image": ""
            },
            {
                "text": "Within-cluster SSE divided by between-cluster SSE",
                "image": ""
            },
            {
                "text": "Total sum of squares divided by between-cluster SSE",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "Gradient descent is a technique we have used to:",
        "answers": [
            {
                "text": "Compute the optimal number of clusters",
                "image": ""
            },
            {
                "text": "Reduce the noise in the training set",
                "image": ""
            },
            {
                "text": "Find the local minima of a function",
                "image": ""
            },
            {
                "text": "Estimate the probability of false positive",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Which of these models is probably overfitting?",
        "answers": [
            {
                "text": "\ud835\udc45' = 0.1 on training, \ud835\udc45' = 0.1 on test",
                "image": ""
            },
            {
                "text": "\ud835\udc45' = 0.8 on training, \ud835\udc45' = 0.7 on test",
                "image": ""
            },
            {
                "text": "\ud835\udc45' = 0.7 on training, \ud835\udc45' = 0.7 on test",
                "image": ""
            },
            {
                "text": "\ud835\udc45' = 0.8 on training, \ud835\udc45' = 0.1 on test",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Laplacian smoothing aims at:",
        "answers": [
            {
                "text": "Improving the feature quality by removing outliers",
                "image": ""
            },
            {
                "text": "Producing readable plots",
                "image": ""
            },
            {
                "text": "Reducing the model\u2019s dependence on the noise",
                "image": ""
            },
            {
                "text": "Avoid penalizing previously unseen observations",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "To visualize a hierarchical clustering one can use:",
        "answers": [
            {
                "text": "a dendrogram ",
                "image": ""
            },
            {
                "text": "a ROC curve",
                "image": ""
            },
            {
                "text": "a boxplot",
                "image": ""
            },
            {
                "text": "a histogram",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "The goal of linear regression is to:",
        "answers": [
            {
                "text": "bring peace to the world",
                "image": ""
            },
            {
                "text": "group similar observations together",
                "image": ""
            },
            {
                "text": "learn a linear function from data ",
                "image": ""
            },
            {
                "text": "evaluate the amount of noise in the data",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Naive Bayes classiers work well for:",
        "answers": [
            {
                "text": "linear programming",
                "image": ""
            },
            {
                "text": "spam filtering ",
                "image": ""
            },
            {
                "text": "k-center clustering",
                "image": ""
            },
            {
                "text": "speech recognition",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "The explained variance of a clustering equals:",
        "answers": [
            {
                "text": "(total variance)/(within-cluster variance)",
                "image": ""
            },
            {
                "text": "(within-cluster variance)/(between-cluster variance)",
                "image": ""
            },
            {
                "text": "(between-cluster variance)/(total variance) ",
                "image": ""
            },
            {
                "text": "(within-cluster variance)/(total variance)",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "A binary classifier on 6 points gives the probabilities: 0.9, 0.85, 0.75, 0.5, 0.4, 0.3; the correct labels are 1,1,0,1,0,0. What is the best probability threshold, if we need FPR <= 1/3?",
        "answers": [
            {
                "text": "0.45 ",
                "image": ""
            },
            {
                "text": "1.0",
                "image": ""
            },
            {
                "text": "0.95",
                "image": ""
            },
            {
                "text": "0.25",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "Mark the wrong statement about gradient descent:",
        "answers": [
            {
                "text": "batch gradient descent approximates \u25bdf using a mini-batch",
                "image": ""
            },
            {
                "text": "stochastic gradient descent approximates \u25bdf with a single example",
                "image": ""
            },
            {
                "text": "there is no guarantee to nd the global minimum",
                "image": ""
            },
            {
                "text": "increasing the learning rate damps oscillations ",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Which task does not require to learn a model?",
        "answers": [
            {
                "text": "Clustering ",
                "image": ""
            },
            {
                "text": "Linear Regression",
                "image": ""
            },
            {
                "text": "Classication",
                "image": ""
            },
            {
                "text": "Logistic Regression",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "For two sets A, B the probability that the first element in a random permutation of A U B is in A \u2229 B:",
        "answers": [
            {
                "text": "is J(A,B) / |A \u2229 B|",
                "image": ""
            },
            {
                "text": "is J(A,B) ",
                "image": ""
            },
            {
                "text": "is 1/|A|+1/|B|",
                "image": ""
            },
            {
                "text": "is 1/(|A||B|)",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "The R^2 and the p-values of a regression:",
        "answers": [
            {
                "text": "are always equivalent",
                "image": ""
            },
            {
                "text": "cannot be both positive",
                "image": ""
            },
            {
                "text": "measure different aspects ",
                "image": ""
            },
            {
                "text": "are negatively correlated",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "xXmini=1k||x-ci||22 is the objective function of:",
        "answers": [
            {
                "text": "k-squares",
                "image": ""
            },
            {
                "text": "k-medians",
                "image": ""
            },
            {
                "text": "k-centers",
                "image": ""
            },
            {
                "text": "k-means",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Classification accuracy is misleading when:",
        "answers": [
            {
                "text": "the label proportions are unbalanced ",
                "image": ""
            },
            {
                "text": "the label proportions are balanced",
                "image": ""
            },
            {
                "text": "the dataset is too small",
                "image": ""
            },
            {
                "text": "the dataset is too large",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "A binary classifier on 6 points gives the probabilities: 0.85, 0.75, 0.65, 0.5, 0.4, 0.2; the correct labels are 1,1,1,0,0,0. What is the best probability threshold?",
        "answers": [
            {
                "text": "0.3",
                "image": ""
            },
            {
                "text": "0.6 ",
                "image": ""
            },
            {
                "text": "0.7",
                "image": ""
            },
            {
                "text": "0.9",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "An algorithm is considered practical if its running time, as a function of the input size, is:",
        "answers": [
            {
                "text": "exponential",
                "image": ""
            },
            {
                "text": "polynomial ",
                "image": ""
            },
            {
                "text": "linear",
                "image": ""
            },
            {
                "text": "logarithmic",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "The naive Bayes classier learns:",
        "answers": [
            {
                "text": "the marginal distribution of predictors",
                "image": ""
            },
            {
                "text": "the joint distribution of predictors",
                "image": ""
            },
            {
                "text": "the joint distribution of predictors and labels ",
                "image": ""
            },
            {
                "text": "the marginal distribution of labels",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "k-PCA differs from k-means in that xi is:",
        "answers": [
            {
                "text": "any PCA component",
                "image": ""
            },
            {
                "text": "any linear combination of PCA components ",
                "image": ""
            },
            {
                "text": "orthogonal to all PCA components",
                "image": ""
            },
            {
                "text": "any a convex combination of PCA components",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "In logistic regression, the estimated probability of xi being a positive is:",
        "answers": [
            {
                "text": "1/(1+e-Tx) ",
                "image": ""
            },
            {
                "text": "1/(1+|x|2)",
                "image": ""
            },
            {
                "text": "log(Tx/(1-Tx))",
                "image": ""
            },
            {
                "text": "log(xi)",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "A sports betting agency wants to predict whether the Italian national football team will or not qualify for the World Cup championship. They should use:",
        "answers": [
            {
                "text": "Clustering",
                "image": ""
            },
            {
                "text": "Logistic regression ",
                "image": ""
            },
            {
                "text": "Linear programming",
                "image": ""
            },
            {
                "text": "Linear regression",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "A problem X  NP is said to be NP-complete if:",
        "answers": [
            {
                "text": "X can be reduced to every Y  NP in polytime",
                "image": ""
            },
            {
                "text": "every Y  NP can be reduced to X in polytime",
                "image": ""
            },
            {
                "text": "no Y  NP can be reduced to X in polytime",
                "image": ""
            },
            {
                "text": "none of the others",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "The quadratic loss of linear regression is:",
        "answers": [
            {
                "text": "i=1m(yi-yi)2",
                "image": ""
            },
            {
                "text": "i=1m(xi-xi)2",
                "image": ""
            },
            {
                "text": "i=1m(xi-yi)2",
                "image": ""
            },
            {
                "text": "i=1m(yi2-yi2)2",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "What is the best threshold value for turning probability scores into binary predictions?",
        "answers": [
            {
                "text": "the one that maximizes sensitivity",
                "image": ""
            },
            {
                "text": "it depends on the problem ",
                "image": ""
            },
            {
                "text": "the one that maximizes accuracy",
                "image": ""
            },
            {
                "text": "the one that maximizes specificity",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "In Human coding, the encoder:",
        "answers": [
            {
                "text": "processes whole runs of identical input symbols",
                "image": ""
            },
            {
                "text": "works by solving a clustering problem",
                "image": ""
            },
            {
                "text": "works by solving a regression problem",
                "image": ""
            },
            {
                "text": "processes each input symbol individually ",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "The Maximum Likelihood Estimator for the parameters of a linear model with independent Gaussian noise is:",
        "answers": [
            {
                "text": "the OLS solution vector *  ",
                "image": ""
            },
            {
                "text": "the square root of the OLS solution *",
                "image": ""
            },
            {
                "text": "it depends on the dataset",
                "image": ""
            },
            {
                "text": "the vector  of the generating process",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "Consider the LP: min f(x,y)=x+y; x+y2; x,y0. The corresponding polytope is:",
        "answers": [
            {
                "text": "degenerate",
                "image": ""
            },
            {
                "text": "bounded",
                "image": ""
            },
            {
                "text": "unbounded",
                "image": ""
            },
            {
                "text": "empty ",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Min-hashing maps each document to:",
        "answers": [
            {
                "text": "one hash signature ",
                "image": ""
            },
            {
                "text": "a distance matrix",
                "image": ""
            },
            {
                "text": "the set of most frequent terms",
                "image": ""
            },
            {
                "text": "a real vector",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "How do you do a linear regression in R?",
        "answers": [
            {
                "text": "predict(y  x, data)",
                "image": ""
            },
            {
                "text": "lm(y  x, data) ",
                "image": ""
            },
            {
                "text": "predict(y  x, data, family=\"binomial\")",
                "image": ""
            },
            {
                "text": "lm(y  x, data, family=\"binomial\")",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "How do you measure the significance of an estimate?",
        "answers": [
            {
                "text": "with its magnitude",
                "image": ""
            },
            {
                "text": "with R^2",
                "image": ""
            },
            {
                "text": "with its p-value",
                "image": ""
            },
            {
                "text": "with its sign",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "A manufacturing company wants to nd out the relationship between the budget spent in advertising and the total sales of the next semester. They could use:",
        "answers": [
            {
                "text": "Linear Regression",
                "image": ""
            },
            {
                "text": "Logistic Regression",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            },
            {
                "text": "Linear Programming",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "The company wants to predict if a machine will have a technical failure in the next 10 days. This could be done with:",
        "answers": [
            {
                "text": "Linear Regression",
                "image": ""
            },
            {
                "text": "Logistic Regression",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            },
            {
                "text": "Linear Programming",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "Moreover, items from the same production line are similar while those from different lines are radically different. You suggest to check by using:",
        "answers": [
            {
                "text": "Linear Regression",
                "image": ""
            },
            {
                "text": "Logistic Regression",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            },
            {
                "text": "Linear Programming",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "What is the true positive rate aka sensitivity?",
        "answers": [
            {
                "text": "the fraction of negatives that are incorrectly classified",
                "image": ""
            },
            {
                "text": "the fraction of negatives that are correctly classified",
                "image": ""
            },
            {
                "text": "the fraction of positives that are incorrectly classified",
                "image": ""
            },
            {
                "text": "the fraction of positives that are correctly classified",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Single-linkage clustering works by:",
        "answers": [
            {
                "text": "repeatedly recomputing the centroids of clusters",
                "image": ""
            },
            {
                "text": "repeatedly merging smaller clusters into larger ones",
                "image": ""
            },
            {
                "text": "enumerating all possible clustering of the given points",
                "image": ""
            },
            {
                "text": "enumerating all possible points in a cluster",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "You have a set of observations (x; y) with x; y 2 R. Which one of the following gives the highest R2?",
        "answers": [
            {
                "text": "Simple linear regression",
                "image": ""
            },
            {
                "text": "Polynomial regression of degree 2",
                "image": ""
            },
            {
                "text": "Polynomial regression of degree 10",
                "image": ""
            },
            {
                "text": "Logistic regression\t",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Which one of the following performances indicates the best model for prediction?",
        "answers": [
            {
                "text": "R2 = 0:2 on training, R2 = 0:1 on test",
                "image": ""
            },
            {
                "text": "R2 = 0:7 on training, R2 = 0:7 on test",
                "image": ""
            },
            {
                "text": "R2 = 0:8 on training, R2 = 0:1 on test",
                "image": ""
            },
            {
                "text": "R2 = 0:9 on training, R2 = \udbc0\udc000:9 on test",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "Which task does not require a training set (i.e. a dataset used for learning a model)?",
        "answers": [
            {
                "text": "Linear Regression",
                "image": ""
            },
            {
                "text": "Logistic Regression",
                "image": ""
            },
            {
                "text": "Classification",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "If you have n points, what is the number of clusters that minimizes the within-cluster sum of squares?",
        "answers": [
            {
                "text": "1",
                "image": ""
            },
            {
                "text": "k",
                "image": ""
            },
            {
                "text": "n",
                "image": ""
            },
            {
                "text": "we cannot say",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "In the bias-variance decomposition of the expected squared error, what does a high bias suggest?",
        "answers": [
            {
                "text": "noisy data",
                "image": ""
            },
            {
                "text": "overtting",
                "image": ""
            },
            {
                "text": "undertting",
                "image": ""
            },
            {
                "text": "crosstting",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "A set of observations (x1; y1), (x2; y2), \u2026,(xn; yn) obeys the law yi := axi + b + i where  i is some random noise. The task of estimating a and b from the dataset is called:",
        "answers": [
            {
                "text": "logistic regression",
                "image": ""
            },
            {
                "text": "linear regression",
                "image": ""
            },
            {
                "text": "linear programming",
                "image": ""
            },
            {
                "text": "logistic programming",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "A regression model (M1) on a training set gives R^2 = 0.5 while a second model (M2) gives R^2 = 0.9. What can we say about predictions on a test set?",
        "answers": [
            {
                "text": "M2 will have error smaller than M1",
                "image": ""
            },
            {
                "text": "M2 will have error larger than M1",
                "image": ""
            },
            {
                "text": "M2 will have the same error as M1",
                "image": ""
            },
            {
                "text": "we cannot say",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "You developed a clinical test to distinguish sick patients from healthy patients. In the population, on average 998 out of 1000 people are healthy, and the test gives an incorrect prediction in 0.5% of the cases. This means the test:",
        "answers": [
            {
                "text": "identifies all the healthy patients",
                "image": ""
            },
            {
                "text": "identifies all the sick patients",
                "image": ""
            },
            {
                "text": "could miss all the healthy patients",
                "image": ""
            },
            {
                "text": "could miss all the sick patients",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "How would you describe overfitting?",
        "answers": [
            {
                "text": "the model is too complex and follows the noise",
                "image": ""
            },
            {
                "text": "the model is too complex and discards the noise",
                "image": ""
            },
            {
                "text": "the model is too simple and follows the noise",
                "image": ""
            },
            {
                "text": "the model is too simple and discards the noise",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "You have to convert the scores given by a logistic regression model into binary predictions. What is the best threshold?",
        "answers": [
            {
                "text": "the one that maximizes accuracy",
                "image": ""
            },
            {
                "text": "the one that maximizes TPR",
                "image": ""
            },
            {
                "text": "the one that maximizes FPR",
                "image": ""
            },
            {
                "text": "it depends on the requirements",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Given a linear regression model, the expected squared error can be usefully decomposed in:",
        "answers": [
            {
                "text": "SSE and SST",
                "image": ""
            },
            {
                "text": "underfit, overfit and noise",
                "image": ""
            },
            {
                "text": "bias, variance, and error",
                "image": ""
            },
            {
                "text": "variance and covariance",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Look at the confusion matrix below (1=positive=true,0=negative=false). What can we say?",
        "answers": [
            {
                "text": "the specificity is 2/3",
                "image": ""
            },
            {
                "text": "the sensitivity is 2/3",
                "image": ""
            },
            {
                "text": "the accuracy is 2/3",
                "image": ""
            },
            {
                "text": "none of the above",
                "image": ""
            }
        ],
        "correct": 0,
        "image": "[encoded data, 14220 characters]"
    },
    {
        "quest": "You are using k-means, and notice that different executions give different results. This happens since:",
        "answers": [
            {
                "text": "k-means is randomized",
                "image": ""
            },
            {
                "text": "clustering can take exponential time",
                "image": ""
            },
            {
                "text": "this is unsupervised learning",
                "image": ""
            },
            {
                "text": "you are using the wrong value for k",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "You have 6 observations; their class (Positive or Negative) and the score given by a logistic regression are as follows: (P,0.9), (P,0.85), (N,0.75), (P,0.5), (N,0.4), (N,0.3). If you do not want the false positive rate of your classier to exceed 1/3, the best choice is to predict \u201cY\" whenever the score is at least:",
        "answers": [
            {
                "text": "1.2",
                "image": ""
            },
            {
                "text": "1.0",
                "image": ""
            },
            {
                "text": "0.45",
                "image": ""
            },
            {
                "text": "0.25",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Logistic regression finds the parameters that maximize: ",
        "answers": [
            {
                "text": "the mean square error of the input data",
                "image": ""
            },
            {
                "text": "the skewness of the input data",
                "image": ""
            },
            {
                "text": "the inter-cluster distance of the input data",
                "image": ""
            },
            {
                "text": "the log-likelihood of the input data",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "What does the Bayesian Optimal Classier need to know in order to work?",
        "answers": [
            {
                "text": "the marginal distribution of each variable",
                "image": ""
            },
            {
                "text": "the marginal distribution of the label",
                "image": ""
            },
            {
                "text": "the joint distribution of variables and label",
                "image": ""
            },
            {
                "text": "the joint distribution of the variables",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Which one of the following classifiers has the best performance?",
        "answers": [
            {
                "text": "TPR=0.2, FPR=0.2",
                "image": ""
            },
            {
                "text": "TPR=0.2, FPR=0.8",
                "image": ""
            },
            {
                "text": "TPR=0.8, FPR=0.2",
                "image": ""
            },
            {
                "text": "TPR=0.8, FPR=0.8",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Your boss calls you to tell your new regression model seems completely useless for prediction, in spite of the high R^2 of the t. You realize that probably there is:",
        "answers": [
            {
                "text": "underfitting",
                "image": ""
            },
            {
                "text": "overfitting",
                "image": ""
            },
            {
                "text": "correlation",
                "image": ""
            },
            {
                "text": "no tomorrow",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "From the confusion matrix below, what can we say?",
        "answers": [
            {
                "text": "R^2 = 0:67",
                "image": ""
            },
            {
                "text": "accuracy = 80%",
                "image": ""
            },
            {
                "text": "all good things must come to an end",
                "image": ""
            },
            {
                "text": "sensitivity < 80%",
                "image": ""
            }
        ],
        "correct": 1,
        "image": "[encoded data, 12868 characters]"
    },
    {
        "quest": "Consider the LP: max f(x,y)=x+3y; x10; y3. The value of the optimal solution is:",
        "answers": [
            {
                "text": "19",
                "image": ""
            },
            {
                "text": "23",
                "image": ""
            },
            {
                "text": "12",
                "image": ""
            },
            {
                "text": "40",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "Your friend proposes a novel clustering algorithm that tries all possible clusterings of the data. This algorithm:",
        "answers": [
            {
                "text": "has exponential complexity",
                "image": ""
            },
            {
                "text": "is efficient but gives poor clusterings",
                "image": ""
            },
            {
                "text": "has polynomial complexity",
                "image": ""
            },
            {
                "text": "is efficient and gives good clusterings",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "In a binary classier build by thresholding the scores of a logistic regression model, the positive observations:",
        "answers": [
            {
                "text": "have a score strictly higher than all the negatives",
                "image": ""
            },
            {
                "text": "have higher density than the negatives",
                "image": ""
            },
            {
                "text": "are at least as many as the negatives",
                "image": ""
            },
            {
                "text": "are separated from the negatives by a hyperplane",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "The class NP contains all problems whose solution:",
        "answers": [
            {
                "text": "can be verified in polytime",
                "image": ""
            },
            {
                "text": "requires exponential time",
                "image": ""
            },
            {
                "text": "none of the others",
                "image": ""
            },
            {
                "text": "can be computed in polytime",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "Lloyd's algorithm for k-means works by:",
        "answers": [
            {
                "text": "evaluating all possible points in a cluster",
                "image": ""
            },
            {
                "text": "evaluating all possible clustering of the points",
                "image": ""
            },
            {
                "text": "repeatedly merging clusters",
                "image": ""
            },
            {
                "text": "repeatedly adjusting the centroids of clusters",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "You want to learn how your revenue depends on parameters such as number of working hours, etc. You could use:",
        "answers": [
            {
                "text": "Linear Regression",
                "image": ""
            },
            {
                "text": "Linear Programming",
                "image": ""
            },
            {
                "text": "Logistic Regression",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "In least squares, R^2 can be seen as:",
        "answers": [
            {
                "text": "the norm of the parameter vector",
                "image": ""
            },
            {
                "text": "none of the others",
                "image": ""
            },
            {
                "text": "the gain over a baseline model",
                "image": ""
            },
            {
                "text": "the inverse of the SSE",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "The ROC curve shows:",
        "answers": [
            {
                "text": "specificity versus sensitivity",
                "image": ""
            },
            {
                "text": "specificity versus FPR",
                "image": ""
            },
            {
                "text": "TPR versus sensitivity",
                "image": ""
            },
            {
                "text": "TPR versus FPR",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Can feature scaling improve the model fitted via least squares?",
        "answers": [
            {
                "text": "yes, in terms of p-values",
                "image": ""
            },
            {
                "text": "no",
                "image": ""
            },
            {
                "text": "yes, in terms of interpretability",
                "image": ""
            },
            {
                "text": "yes, in terms of R2",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Can a clustering on n points achieve 0 within-cluster sum of squares?",
        "answers": [
            {
                "text": "yes, with 1 cluster",
                "image": ""
            },
            {
                "text": "yes, with k clusters",
                "image": ""
            },
            {
                "text": "yes, with n clusters",
                "image": ""
            },
            {
                "text": "no, never",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "In linear regression, if the p-value for the estimate i is small enough, then we:",
        "answers": [
            {
                "text": "accept the null hypothesis i = 0",
                "image": ""
            },
            {
                "text": "reject the null hypothesis i = 0",
                "image": ""
            },
            {
                "text": "use a model with more features",
                "image": ""
            },
            {
                "text": "use a model with more parameters",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "Texts written in the same language have a similar letter frequency distribution. You can check this fact by:",
        "answers": [
            {
                "text": "Logistic Regression",
                "image": ""
            },
            {
                "text": "Linear Programming",
                "image": ""
            },
            {
                "text": "Linear Regression",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Texts written in the same language have a similar letter frequency distribution. You can check this fact by:",
        "answers": [
            {
                "text": "Logistic Regression",
                "image": ""
            },
            {
                "text": "Linear Programming",
                "image": ""
            },
            {
                "text": "Linear Regression",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Two classifiers, C1 and C2, have accuracy respectively 98\\\\% \\\\and 95%. Which one is the best?",
        "answers": [
            {
                "text": "C1",
                "image": ""
            },
            {
                "text": "They are equivalent",
                "image": ""
            },
            {
                "text": "We cannot say",
                "image": ""
            },
            {
                "text": "C2",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Correlation clustering asks to minimize:",
        "answers": [
            {
                "text": "The root mean squared error",
                "image": ""
            },
            {
                "text": "The number of disagreements",
                "image": ""
            },
            {
                "text": "The intra-cluster variance",
                "image": ""
            },
            {
                "text": "The running time",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "If you increase the complexity of your linear regression model, eventually the SSE on the test set will:",
        "answers": [
            {
                "text": "Approach zero",
                "image": ""
            },
            {
                "text": "Cancel the training error",
                "image": ""
            },
            {
                "text": "Exceed the training error",
                "image": ""
            },
            {
                "text": "Become negative",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Classification accuracy is misleading when:",
        "answers": [
            {
                "text": "The label proportions are unbalanced",
                "image": ""
            },
            {
                "text": "The dataset is too small",
                "image": ""
            },
            {
                "text": "The label proprtions are balanced",
                "image": ""
            },
            {
                "text": "The dataset is too large",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "The worst-case running time of the k-means algorithm on the n points is:",
        "answers": [
            {
                "text": "Polynomial in n",
                "image": ""
            },
            {
                "text": "Superpolynomial in n",
                "image": ""
            },
            {
                "text": "Linear in n",
                "image": ""
            },
            {
                "text": "Unbounded in n",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "In linear regression, the expected squared error s the sum of:",
        "answers": [
            {
                "text": "The good the bad and the ugly",
                "image": ""
            },
            {
                "text": "Squared bias and variance and noise",
                "image": ""
            },
            {
                "text": "Underfit and overfit the noise",
                "image": ""
            },
            {
                "text": "Variance and covariance and noise",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "Your friend proposes an innovative clustering algorithm that enumerates all possible clusterings of the points. This algorithm:",
        "answers": [
            {
                "text": "Has exponential complexity",
                "image": ""
            },
            {
                "text": "Has polynomial complexity",
                "image": ""
            },
            {
                "text": "Is efficient but gives poor clustering",
                "image": ""
            },
            {
                "text": "Is efficient and gives good clusterings",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "A high R^2 on a given dataset means:",
        "answers": [
            {
                "text": "A large error on new data",
                "image": ""
            },
            {
                "text": "A large error on that data",
                "image": ""
            },
            {
                "text": "A small error on that data",
                "image": ""
            },
            {
                "text": "A small error on new data",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Multicollinearity arises if the features vectors are:",
        "answers": [
            {
                "text": "absolutely orthonogal",
                "image": ""
            },
            {
                "text": "linearly dependent",
                "image": ""
            },
            {
                "text": "linearly independent",
                "image": ""
            },
            {
                "text": "positive semidefinite",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "A logistic regression model learns:",
        "answers": [
            {
                "text": "The conditional distribution of predictors",
                "image": ""
            },
            {
                "text": "The conditional distribution of labels",
                "image": ""
            },
            {
                "text": "The marginal distribution of predictors",
                "image": ""
            },
            {
                "text": "The marginal distribution of labels",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "Consider the LP: min f(x,y) = x + y; x+y >= 2; x, y <= 0. The corresponding polytope is:",
        "answers": [
            {
                "text": "Bounded",
                "image": ""
            },
            {
                "text": "empty",
                "image": ""
            },
            {
                "text": "Degenerate",
                "image": ""
            },
            {
                "text": "Unbounded",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "To measure the efficiency of algorithms we use:",
        "answers": [
            {
                "text": "convex analysis",
                "image": ""
            },
            {
                "text": "asymptotic analysis",
                "image": ""
            },
            {
                "text": "squared analysis",
                "image": ""
            },
            {
                "text": "clinical analysis",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "Everything else being equal, what does suggest a good clustering?",
        "answers": [
            {
                "text": "a high p-value",
                "image": ""
            },
            {
                "text": "a low within-cluster sum of squares",
                "image": ""
            },
            {
                "text": "a large number of observations",
                "image": ""
            },
            {
                "text": "a small number of clusters",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "The set cover problem:",
        "answers": [
            {
                "text": "Can be solved in constant time",
                "image": ""
            },
            {
                "text": "is part of linear programming",
                "image": ""
            },
            {
                "text": "is NP-Complete",
                "image": ""
            },
            {
                "text": "is P-Complete",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "A company must allocate 5M\u20ac so that each department receives a minimum amount. You can use:",
        "answers": [
            {
                "text": "Linear regression",
                "image": ""
            },
            {
                "text": "Logistic Regression",
                "image": ""
            },
            {
                "text": "Clustering",
                "image": ""
            },
            {
                "text": "Linear Programming",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "With hierarchical clustering on n points you can get:",
        "answers": [
            {
                "text": "Between 1 and n clusters",
                "image": ""
            },
            {
                "text": "No satisfaction",
                "image": ""
            },
            {
                "text": "Up to 2^n clusters",
                "image": ""
            },
            {
                "text": "At most log(n) clusters",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    },
    {
        "quest": "The standard assumption of linear regression is that the noise across the observations:",
        "answers": [
            {
                "text": "is fast and furios",
                "image": ""
            },
            {
                "text": "is always bounded",
                "image": ""
            },
            {
                "text": "is Gaussian and correlated",
                "image": ""
            },
            {
                "text": "is Gaussian and independent",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "The ROC curve is used to measure:",
        "answers": [
            {
                "text": "The amount of overfitting and underfitting",
                "image": ""
            },
            {
                "text": "The noise in the training dataset",
                "image": ""
            },
            {
                "text": "The performance of binary classifiers",
                "image": ""
            },
            {
                "text": "The MSE obtained by a linear regression\"",
                "image": ""
            }
        ],
        "correct": 2,
        "image": ""
    },
    {
        "quest": "Geometrically, each constraint of a linear program corresponds to:",
        "answers": [
            {
                "text": "a vector",
                "image": ""
            },
            {
                "text": "A double-space",
                "image": ""
            },
            {
                "text": "a cone",
                "image": ""
            },
            {
                "text": "a half-space",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Many well-known clustering problems are:",
        "answers": [
            {
                "text": "impossible to solve",
                "image": ""
            },
            {
                "text": "NP-hard",
                "image": ""
            },
            {
                "text": "easy to solve",
                "image": ""
            },
            {
                "text": "infeasible\"",
                "image": ""
            }
        ],
        "correct": 1,
        "image": ""
    },
    {
        "quest": "A polytope is:",
        "answers": [
            {
                "text": "The difference of half-spaces",
                "image": ""
            },
            {
                "text": "the greatest gift of all",
                "image": ""
            },
            {
                "text": "the union of half-spaces",
                "image": ""
            },
            {
                "text": "The intersection of half spaces",
                "image": ""
            }
        ],
        "correct": 3,
        "image": ""
    },
    {
        "quest": "Everything else being equal. What does suggest good clustering?",
        "answers": [
            {
                "text": "Few clusters",
                "image": ""
            },
            {
                "text": "low within-cluster sum of squares",
                "image": ""
            },
            {
                "text": "high p-value",
                "image": ""
            },
            {
                "text": "large number of points",
                "image": ""
            }
        ],
        "correct": 0,
        "image": ""
    }
]