
STA 4273H Winter 2015  Lectures
Video Archive
here.
Lecture Schedule
 Lecture 1  Machine Learning:
Introduction to Machine Learning, Linear Models for Regression
(notes
[pdf], [Video] )
Reading: Bishop, Chapter 1: sec. 1.1  1.5.
and Chapter 3: sec. 1.1  1.3.
Optional: Bishop, Chapter 2: Backgorund material;
Hastie, Tibshirani, Friedman, Chapters 2 and 3.
 Lecture 2  Bayesian Framework:
Bayesian Linear Regression, Evidence Maximization.
Linear Models for Classification.
(notes
[pdf],
[Video] )
Reading: Bishop, Chapter 3: sec. 3.3  3.5. Chapter 4.
Optional:
Radford Neal's NIPS tutorial on
Bayesian Methods for Machine Learning:
[pdf]).
Also see Max Welling's notes on
Fisher Linear Discriminant Analysis
[pdf
]
 Lecture 3  Classification
Linear Models for Classification, Generative and
Discriminative approaches, Laplace Approximation.
(notes
[pdf],
[Video] )
Reading: Bishop, Chapter 4.
Optional:
Hastie, Tibshirani, Friedman, Chapter 4.
 Lecture 4  Graphical Models:
Bayesian Networks, Markov Random Fields
(notes
[pdf]
[Video]
)
Reading: Bishop, Chapter 8.
Optional:
Hastie, Tibshirani, Friedman, Chapter 17 (Undirected Graphical Models).
MacKay, Chapter 21 (Bayesian nets) and Chapter 43 (Boltzmann mchines).
Also see this paper on
Graphical models, exponential families, and variational inference by
M. Wainwright and M. Jordan, Foundations and Trends in Machine Learning,
[ here
]
 Lecture 5  Mixture Models and EM:
Mixture of Gaussians, Generalized EM, Variational Bound.
(notes
[pdf],
[Video])
Reading: Bishop, Chapter 9.
Optional:
Hastie, Tibshirani, Friedman, Chapter 13 (Prototype Methods).
MacKay, Chapter 22 (Maximum Likelihood and Clustering).
 Lecture 6  Variational Inference
MeanField, Bayesian Mixture models, Variational Bound.
(notes
[pdf],
[Video])
Reading: Bishop, Chapter 10.
Optional:
MacKay, Chapter 33 (Variational Inference).
 Lecture 7  Sampling Methods
Rejection Sampling, Importance sampling, MH and Gibbs.
(notes
[pdf],
[Video])
Reading: Bishop, Chapter 11.
Optional:
MacKay, Chapter 29 (Monte Carlo Methods).
 Lecture 8  Continuous Latent Variable Models
PCA, FA, ICA, Deep Autoencders
(notes
[pdf],
[Video])
Reading: Bishop, Chapter 12.
Optional:
Hastie, Tibshirani, Friedman, Chapters 14.5, 14.7, 14.9 (PCA, ICA,
nonlinear dimensionality reduction).
MacKay, Chapter 34 (Latent Variable Models).
 Lecture 9  Modeling Sequential Data
HMMs, LDS, Particle Filters.
(notes
[pdf])
Reading: Bishop, Chapter 13.
 March 23  Student Presentations
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STA 4273H (Winter 2015): Large Scale Machine Learning
 http://www.utstat.toronto.edu/~rsalakhu/STA4273_2015/
