
CSC2515 Fall 2006  Lectures
Tentative Lecture Schedule
 Sept 12  Machine Learning:
Introduction to Machine Learning, Generalization and Capacity
(notes [ps.gz]
[pdf])
 Sept 19 Classification 1:
KNN, linear discriminants, decision trees
(notes [ps.gz]
[pdf])
 Sept 19  TUTORIAL (Prob/Stats/Linear Algebra Review/Questions)
 Sept 25  Classification 2:
probabilistic classifiers: classconditional Gaussians,
naive Bayes, logistic regression, neural nets for classification
(notes [ps.gz]
[pdf])
 Oct 3: Assignment 1 (Classification) posted
 Oct 3  Regression 1:
constant model, linear models, generalized additive models
(e.g. RBFs), locally weighted regression,
multilayer perceptrons/neural networks
(notes [ps.gz]
[pdf])
 Oct 10  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 10  TUTORIAL (A1 Questions)
 Oct17: Assignment 1 due at the start of class
 Oct 17  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])
 Oct24: Assignment 2 (Regression) posted
 Oct 24  Unsupervised Learning 1:
Trees & Clustering
Kmeans, heirarchical clustering (alglomerative and divisive),
maximum likelihood trees, optimal tree structure
(notes [ps.gz]
[pdf])
 Oct 31  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])
 Oct 31  TUTORIAL (A2 Questions)
 Nov 7: Assignment 2 due
 November 7  Unsupervised Learning 3:
Continuous latent variable models, Factor Analysis, (Probabilistic)
PCA, Mixtures of Factor Analyzers, Independent Components Analysis
(notes [ps.gz]
[pdf])
 Nov 14: Assignment 3 posted
 Nov 14  Time Series Models
autoregressive/Markov models, aggregate Markove models,
hidden Markov models, profile HMMs
(notes [ps.gz]
[pdf])
 Nov 21  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 21  TUTORIAL (A3 Questions)
 Nov 28: Assignment 3 due at the start of class
 Nov 28  MetaLearning Methods:
stacking, bagging, boosting
(notes [ps.gz]
[pdf])
 Nov 28: Project Meetings during tutorial time after class
 Dec 1 12:002:00pm, Bahen 1220 (NOTE UNUSUAL DATE/TIME/LOCATION)
 Kernel methods:
the kernel trick, support vector machines, kernel perceptrons,
sparsity, capacity control, dual problems
(notes [ps.gz]
[pdf])
 Dec 5  NO CLASS (rescheduled to Dec1)
 December 15  projects due by email before noon
Send attachments or valid URL pointing to your report.
POSTSCRIPT or PDF only. DO NOT SUBMIT WORD, HTML OR OTHER FORMAT
FILES.
 December 15  all readings must be completed by noon.
Online Reading Submission
 Extra topics we may or may not have time for
[
Home 
Course Information 
Lecture Schedule/Notes 
Textbook/Readings 
Assignments/Project 
Computing 
]
CSC2515  Machine Learning  www.cs.toronto.edu/~roweis/csc2515/
