This page lists the readings for each lecture. The instructors will include comments and pointers to other resources that might be helpful to get the most out of the readings.

Mon., Sep. 10:

  • (Bishop - 2.1) This section gives many details on the Bayesian and maximum likelihood results for the binomial example Carlos covered today.

Tue., Sep. 11:

Recitation 1 -- Probability Review

Wed., Sep. 12:

  • (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 - 1.5.5) Covers loss functions for regression and discusses minimizing expected loss
  • Completely Optional: Joey's quickly written notes on the matrix MLE for regression. Corrections are welcome. [PDF] [Mathematica6 Notebook]

Mon., Sep 17:

Wed., Sep 19:

Mon., Sep 24:

  • Bishop - 4.0, 4.2, 4.3, 4.4, 4.5

Wed., Sep. 26:

  • (Bishop - 1.6) Information Theory
  • (Bishop - 14.4) Tree-based Models
  • Recommended Reading: Quantities of Information Wikipedia entry
  • 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)

Mon., Oct. 1:

Wed., Oct. 3:

Mon., Oct. 8:

  • (Bishop 5.1) Feed-forward Network Functions
  • (Bishop 5.2) Network Training
  • (Bishop 5.3) Error Backpropagation

Wed., Oct. 10:

  • (Bishop 2.5) Nonparametric Methods

Mon., Oct. 15:

Wed., Oct. 17:

Mon., Oct. 22:

Wed., Oct. 24:

Mon., Oct. 29:

  • (Bishop 8.1,8.2) Bayesian Networks, Conditional Independence

Wed., Oct. 31:

  • (Bishop 8.4.1,8.4.2) Inference in Chain/Tree structures

Mon., Nov. 5:

Wed., Nov. 7:

Mon., Nov. 12:

  • (Bishop 9.1, 9.2) K-means, Mixtures of Gaussians

Wed., Nov. 21:

  • NO CLASS: Thanksgiving