
CSC2515 Fall 2002  Lectures
Tentative Lecture Schedule
 Sept 11
Lecture 1: Introduction to Machine Learning, Generalization and Capacity
(notes)
 Sept 18 Classification 1:
KNN, linear discriminants, decision trees
(notes)
 Sept 25  Classification 2:
naive Bayes, logistic regression, neural nets for classification
(notes)
 Sept. 30: Assignment 1 (Classification) posted
 Oct 2  Regression 1:
constant model, linear models, generalized additive models
(e.g. RBFs), locally weighted regression,
multilayer perceptrons/neural networks
(notes)
 Oct 9  Regression 2 and Basic Optimization:
error surfaces, weight space, credit assignment problem,
neural networks, kolmogorov's theorem,
backprop algorithm for efficiently computing gradients,
gradient descent, stochastic gradient,
(notes)
 October 15: Assignment 2 (Regression) handed out
 October 16: Assignment 1 due at the start of class
 Oct 16  Supervised Mixtures and Advanced Optimization:
conjugate gradient, bound optimization , convexity, enforcing constraints
mixtures of experts, piecewise linear models
(notes)
 Oct 23  Unsupervised Learning 1:
Mixture models and the EM Algorithm:
missing data, hidden variables,
Jensen's inequality, lower bound on marginal likelihood,
free energy interpretation, inference,
(notes)
 Oct30  Unsupervised Learning 2: Trees & Clustering
Kmeans, heirarchical clustering, soft competitive learning
(Mixtures of Gaussians), maximum likelihood trees, optimal tree structure
(notes)
 November 6: Assignment 2 due
 November 6: Assignment 3 posted
 November 6  Unsupervised Learning 3:
Continuous latent variable models, Factor Analysis, (Probabilistic)
PCA, Mixtures of Factor Analyzers, Independent Components Analysis
(notes)
 Nov 13  Time Series Models
autoregressive/Markov models, hidden Markov models
(notes)
 Nov 20: Assignment 3 due at the start of class
 Nov 20  Capacity Control:
generalization and overfitting, No free lunch theorems,
high dimensional issues.
capacity control methods: weight decay,
early stopping, cross validation, model averaging, intro to Bayesianism
(notes)
 Nov 27  MetaLearning Methods:
stacking, bagging, boosting
(notes)
 Dec 4  Kernel methods:
the kernel trick, support vector machines, kernel perceptrons,
sparsity, capacity control, dual problems
(notes)
 Dec 18  projects due
 Extra topics we didn't have time for
 other kernel machines: gaussian processes
 linear dynamical systems, Kalman filtering
 Approximate inference and learning:
sampling, variational approximations, loopy belief propagation
 Spectral Methods:
Isomap,LLE, spectral clustering,
 MaxEnt models:
maximum entropy/energybased models, iterative scaling,
products of experts, dependency nets,
 Matrix Factorizations:
aspect models/LDA/plaids, nonnegative matrix factorization
 Automatic Structure Learning:
sparsity priors, empirical Bayes,
automatic relevance determination (MLII), structural EM
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CSC2515  Machine Learning  www.cs.toronto.edu/~roweis/csc2515/
