# Readings - 10-701 Machine Learning

• (Bishop - 2.1) This section gives many details on the Bayesian and maximum likelihood results for the binomial example Carlos covered today.
• (Bishop - 1.2) A good review of the probability concepts needed for this course
• We have not checked all of these articles for correctness, but we do recommend brushing up with the Wikipedia articles for these topics:
• (Bishop - 1.1 to 1.4) Introduces curve fitting, reviews probability theory, introduces Gaussians, and covers the famous "curse of dimensionality"
• (Bishop - 3.1, 3.1.1, 3.1.4, 3.1.5, 3.2, 3.3, 3.3.1, 3.3.2) Regression, linear basis function models, bias-variance decomposition, and Bayesian linear regression
• (Bishop - 3.2) Bias-variance decomposition
• (Bishop - 1.5.5) Covers loss functions for regression and discusses minimizing expected loss
• (Bishop - 1.3) Discusses model selection using a test set
• Mitchell Chapter (Sections 1 and 2): Mitchell's Chapter on Naive Bayes and Logistic Regression
• (Bishop - 14.4) Tree-based Models
• Recommended Reading: Nils Nilsson's Chapter (All Sections): Decision Trees
• Optional Review of Boolean Logic/DNF: Nils Nilsson's Chapter Boolean Functions (first 4 pages)
• (Bishop - 14.3) Boosting
• Schapire's Boosting Tutorial
• (Bishop - 1.3) Model Selection (Cross Validation)
• (Bishop 1.3) Model Selection / Cross Validation
• (Bishop 3.1.4) Regularized least squares
• (Bishop 5.1) Feed-forward Network Functions
• (Bishop 5.1) Feed-forward Network Functions
• (Bishop 5.2) Network Training
• (Bishop 5.3) Error Backpropagation
• (Bishop 2.5) Nonparametric Methods
• (Mitchell Chapter 7) Computational Learning Theory
• (Bishop 8.1,8.2) Bayesian Networks, Conditional Independence
• (Bishop 9.1, 9.2) K-means, Mixtures of Gaussians
• (Bishop 9.3, 9.4) EM