Topics for Take-Home Test 3

    General questions on methods/concepts covered during the class:
    (short-answer questions on general qualitative properties)
        Supervised vs unsupervised learning
        Two-class Bayes discriminant function based on 2 Gaussian distributions
        Linear discriminant
        Perceptron
        Logistic Regression
        PCA
        K-means
	Expectation-Maximization
        K-nearest neighbor
        Support vector machines:
            linear vs non-linear kernal
            hard vs soft margin
	Multilayer Perceptrons: 
	    convolutional networks
	    auto-encoders
	    LSTMS and GANs
	Decision Trees & Random Forests

    Chapter 9: Decision Trees
        9.1: Introduction
        9.2: Univariate Trees
        Notes on random forests
        Notes on information theory

    Chapter 11: Multilayer Perceptrons
        11.1: Introduction
        11.2: The Perceptron
        11.3: Training a Perceptron
        11.4: Learning Boolean Functions
        11.5: Multilayer Perceptrons
        11.7: Backpropagation Algorithm
        11.8: Training Procedures
        11.11: Dimensionality Reduction
        11.12: Learning Time
        11.13: Deep Learning