Contents
Supervised learning
1. Linear Regression
1.1 LMS algorithm
1.2 The normal equations
1.2.1 Matrix derivatives
1.2.2 Least squares revisited
1.3 Probabilistic interpretation
1.4 Locally weighted linear regression
2. Classification and logistic regression
2.1 Logistic regression
2.2 Digression: The perceptron learning algorithm
2.3 Another algorithm for maximizing L(theta)
3. Generalized Linear Models
3.1 The exponential family
3.2 Constructing GLMs
4. Generative Learning algorithms
4.1 Gaussian discriminant analysis
4.2 Naïve Bayes
5. Support Vector Machines
5.1 Margins: Intuition
5.2 Notation
5.3 Functional and geometric margins
5.4 The optimal margin classifier
5.5 Lagrange duality
5.6 Optimal margin classifiers
5.7 Kernels
5.8 Regularization and not-separable case
5.9 The SMO algorithm
Ml advices
6. Learning Theory
6.1 Bias/Variance tradeoff
6.2 Preliminaries
6.3 The case of finite H
6.4 The case of infinite H
7. Regularization and model selection
7.1 Cross validation
7.2 Feature Selection
7.3 Bayesian statistics and regularization
7.4 The perceptron and large margin classfiers
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