From Computational Learning and Motor Control Lab

Teaching: Syllabus: Advanced Topics in Neural Computation and Statistical Learning

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Date

Topic

Assignments

Aug. 27

Introduction
Background Readings:


Linear Algebra Refresher,
Statistics Refresher,
Issues in Neural Networks

Sept. 3

An Introduction to Graphical Models and Statistical Estimation

Ch.2, Ch.3, Ch.5, Questions Ch.2 & Ch.3, Questions Ch.5

Sept. 10

Refreshing Classification and Regression

Ch.6, Ch.7, Questions Ch.6, Questions Ch.7

Sept. 17

Maximum Likelihood Estimation & Mixture Models

Ch.10, Ch.13

Sept. 24

The Theory of the EM Algorithm

Ch.10, Ch.11, Questions Ch.10, Questions Ch.11, EM-background slides

Oct.1

The EM Algorithm applied to mixture models and factor analsis

Paper on Factor Analysis, Ch.14, Derivation of Factor Analysis

Oct. 8

Bayesian Model Estimation: Basics & Gaussian Approximation

B_Ch10 Paper on Bayesian PCA, Handout on Bayesian Factor Analysis

Oct. 15

Bayesian Model Estimation: Variational Approximations

Variational Tutorial, Variational Bayes, Calculus of Variations, Variational Bayesian Factor Analysis

Oct 22

Bayesian Model Estimation: Variational Methods (continued), and

of Markov Chain Monte Carlo Methods

Variational PCA Paper, MacKay Ch32 & Ch33 B_CH10.9

Oct. 29

Hidden Markov Models

Ch.12, HMM-paper

Nov. 5

Hidden Markov Models (cont'd)

Ch.15, HMM-paper

Nov. 12

Kalman Filters, Particle Filters

Ch.15, Intro-Paper Applied-Paper

Nov. 19

Gaussian Processes Paper_MacKay,

Paper_Williams, Questions

Nov. 26

Support Vector Machines Tutorial_Burges,

Tutorial SVM Regression, Class notes

Dec.3

Student Presentations

All book chapter in PS and PDF format:

Retrieved from http://www-clmc.usc.edu/Teaching/TeachingAdvancedTopicsInNeuralComputationAndStatisticalLearningSyllabus
Page last modified on January 25, 2006, at 06:59 PM