Problem 1: Visualization [3 points] Match the following chart types: (a) scatter plot, (b) heatmap, (c) 3D plot (e.g., volume or isosurface rendering), (d) histogram, (e) bar plot to the following visualization tasks: distribution of height values in a population of persons: - distribution of left-handed, right-handed, and ambidextrous persons: consumption of ice cream as a function of outdoor temperature and precipitation: - relationship between amount of tobacco consumed and rate of cancer: - temperature as a function of latitude, longitude, and altitude: Problem 2: Parametric and Non-Parametric Models [4 points] Match the following models: (a) linear regression with 1 feature variable, (b) quadratic regression with 1 feature variable, (0) multivariate linear regression with 3 feature variables, (d) multi-layer perceptron with 'l feature variable and two 10-neuron hidden layers, (e) knearest neighbor with k=4, (f) multi-layer perceptron with 1 feature variable and one 25-neuron hidden layer, to the number of parameters that the models have (assuming that each model predicts one output label): - non-parametric: 2 parameters: - 3 parameters: - 4 parameters: - 76 parameters: - 141 parameters: Problem 3: k-Nearest Neighbors Decision [3 points] In a binary classification problem, suppose that for a test example, the seven nearest training examples (ordered by distance from nearest to farthest away) have labels 0, 1, 1, 0, 0, 1, 1. What label would kNN predict for this test example with k=3? What would it predict with k=5? Briey explain your answers