Lecture note 1. Supervised Learning, Discriminative Algorithms
    Part I
    Linear Regression  ----------------------------------   page 3
        1 LSM algorithm ---------------------------------   page 4
        2 The normal equations --------------------------   page 7
            2.1 Matrix derivatives ----------------------   page 8
            2.2 Least squares revisited -----------------   page 9
        3 Probabilistic interpretation ------------------   page 11
        4 Locally weighted linear regression ------------   page 13
    Part II
    Classification and logistic regression --------------   page 16
        5 Logistic regression ---------------------------   page 16
        6 Digression: The perceptron algorithm-----------   page 19
        7 Another algorithm for maximizing l()-----------   page 20
    Part III
    Generalize Linear Models ----------------------------   page 22
        8 The exponential family ------------------------   page 22
        9 Constructing GLMs -----------------------------   page 24
            9.1 Ordinary Least Squares ------------------   page 25
            9.2 Logistic Regression ---------------------   page 26
            9.3 Softmax Regresion -----------------------   page 26

Lecture note 2. Generative algorithms
    Part IV
    Generative Learning Algorithms ----------------------   page 1
        1 Gaussian Discriminant Analysis ----------------   page 2
            1.1 The Multivariate Normal Distribution ----   page 2
            1.2 The Gaussian Discirminant Analysis ------   page 5
            1.3 Discussion: GDA and Logistic Regression -   page 6
        2 Naive Bayes -----------------------------------   page 8
            2.1 Laplace Smoothing -----------------------   page 11
            2.2 Event Models for Text Classification ----   page 13

Lecture note 3. SVM
    Part V
    Support Vector Machines -----------------------------   page 1
        1. Margins: Intuition ---------------------------   page 1
        2. Notation -------------------------------------   page 3
        3. Functional and Geometric Margins -------------   page 3
        4. The Optimal Margin Classifier ----------------   page 5
        5. Lagrange Duality -----------------------------   page 7
        6. Optimal Margin Classifiers -------------------   page 10
        7. Kernels --------------------------------------   page 13
        8. Regularization and the Non-separable case ----   page 19
        9. The SMO algorithm ----------------------------   page 20
            9.1 Coordinate Ascent -----------------------   page 21
            9.2 SMO -------------------------------------   page 22

Lecture note 4. Learning Theory
    Part VI
    Learning Theory -------------------------------------   page 1
        1. Bias/Variance Tradeoff -----------------------   page 1
        2. Preliminaries --------------------------------   page 2
        3. The Case of Fininte H (Hypothesis Class) -----   page 5
        4. The Case of Infininte H (Hypothesis Class) ---   page 8

Lecture note 5. Regularization and Model Selection
    Part VII
    Regularization and Model Selection ------------------   page 1
        1. Cross Validation -----------------------------   page 2
        2. Feature Selection ----------------------------   page 4
        3. Bayesian Statistics and Regularization -------   page 6

Lecture note 6. The Perceptron Algorithm
        1 The Perceptron and Large Margin Classifiers ---   page 1

Lecture notoe 7(a). Unsupervised Learning, k-means clustering.
        The k-means Clustering Algorithm ----------------   page 1

Lecture note 7(b). Mixture of Gaussians
        Mixture of Gaussians and the EM Algorithm -------   page 1

Lecture note 8. The EM Algorithms
    Part IX
    The EM Algorithm
        1. Jensen's Inequality --------------------------   page 1
        2. The EM Algorithm -----------------------------   page 2
        3. Mixture of Gaussian Revisited ----------------   page 6

Lecture note 9. Factor Analysis
    Part X
    Factor Analysis
        1. Restrictions of Sigma ------------------------   page 2
        2. Marginals and Conditionals of Gaussians ------   page 3
        3. The Factor Analysis Model --------------------   page 4
        4. EM for Factor Analysis -----------------------   page 6

Lecture note 10. Principal Components Analysis
    Part XI
    Principal Component Analysis ------------------------   page 1

Lecture note 11. Independent Components Analysis 
    Part XII
    Independent Components Analysis ---------------------   page 1
        1. ICA Ambiguities ------------------------------   page 2
        2. Densities and Linear Transformations ---------   page 3
        3. ICA Algorithm --------------------------------   page 4

Lecture note 12. Reinforcement Learning and Control 
    Part XIII
    Reinforcement Learning and Control ------------------   page 1
        1. Markov Decision Processes --------------------   page 2
        2. Value Iteration and Policy Iteration ---------   page 4
        3. Learning a Model for an MDP ------------------   page 6
        4. Continuous State MDPs ------------------------   page 7
            4.1 Discretization --------------------------   page 8
            4.2 Value Function Approximation ------------   page 10
                4.2.1 Using a Model of Simulator --------   page 10
                4.2.2 Fitted Value Iteration ------------   page 12