
CSC2515 Fall 2005  Lectures
Tentative Lecture Schedule
 Sept 13  Machine Learning:
Introduction to Machine Learning, Generalization and Capacity
(notes [ps.gz]
[pdf])
 Sept 20 Classification 1:
KNN, linear discriminants, decision trees
(notes [ps.gz]
[pdf])
 Sept 20  TUTORIAL (Prob/Stats/Linear Algebra Review/Questions)
 Sept 26  Classification 2:
probabilistic classifiers: classconditional Gaussians,
naive Bayes, logistic regression, neural nets for classification
(notes [ps.gz]
[pdf])
 Oct 4: Assignment 1 (Classification) posted
 Oct 4  Regression 1:
constant model, linear models, generalized additive models
(e.g. RBFs), locally weighted regression,
multilayer perceptrons/neural networks
(notes [ps.gz]
[pdf])
 Oct 11  Objective Functions and Optimization:
error surfaces, weight space, gradient descent, stochastic gradient,
conjugate gradients, second order methods, convexity, enforcing constraints
(notes [ps.gz]
[pdf])
 Oct 11  TUTORIAL (A1 Questions)
 Oct18: Assignment 1 due at the start of class
 Oct 18  Regression 2 and Supervised Mixtures:
credit assignment problem, neural networks, radial basis networks,
kolmogorov's theorem,
backprop algorithm for efficiently computing gradients,
mixtures of experts, piecewise models
(notes [ps.gz]
[pdf])
 Oct25: Assignment 2 (Regression) posted
 Oct 25  Unsupervised Learning 1:
Trees & Clustering
Kmeans, heirarchical clustering (alglomerative and divisive),
maximum likelihood trees, optimal tree structure
(notes [ps.gz]
[pdf])
 Nov 1  Unsupervised Learning 2:
Mixture models and the EM Algorithm:
missing data, hidden variables,
Jensen's inequality, lower bound on marginal likelihood,
free energy interpretation, inference,
(notes [ps.gz]
[pdf])
 Nov 1  TUTORIAL (A2 Questions)
 Nov 8: Assignment 2 due
 November 8  Unsupervised Learning 3:
Continuous latent variable models, Factor Analysis, (Probabilistic)
PCA, Mixtures of Factor Analyzers, Independent Components Analysis
(notes [ps.gz]
[pdf])
 Nov 15: Assignment 3 posted
 Nov 15  Time Series Models
autoregressive/Markov models, aggregate Markove models,
hidden Markov models, profile HMMs
(notes [ps.gz]
[pdf])
 Nov 22  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 [ps.gz]
[pdf])
 Nov 22  TUTORIAL (A3 Questions)
 Nov 29: Assignment 3 due at the start of class
 Nov 29  MetaLearning Methods:
stacking, bagging, boosting
(notes [ps.gz]
[pdf])
 Dec 2 (UNUSUAL TIME 10am, room UC163)  Kernel methods:
the kernel trick, support vector machines, kernel perceptrons,
sparsity, capacity control, dual problems
(notes [ps.gz]
[pdf])
 Dec 6  NO CLASS (rescheduled to Dec2)
 December 19  projects due by email before 9am
Send attachments or valid URL pointing to your report.
POSTSCRIPT or PDF only. DO NOT SUBMIT WORD, HTML OR OTHER FORMAT
FILES.
 December 19  all readings must be completed by 9am.
Online Reading Submission
 Extra topics we may or may not have time for
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CSC2515  Machine Learning  www.cs.toronto.edu/~roweis/csc2515/
