Large Scale Machine Learning - UToronto - STA 4273H Winter 2015



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large_scale_machine_learning_utoronto_2015 (18 files)
lecture1.ogv390.00MB
Lecture1_2015.pdf8.72MB
lecture2.ogv292.45MB
Lecture2_2015.pdf4.40MB
lecture3.ogv314.65MB
Lecture3_2015.pdf4.94MB
lecture4.ogv331.26MB
Lecture4_2015.pdf14.39MB
lecture5.ogv342.56MB
Lecture5_2015.pdf7.78MB
lecture6.ogv375.90MB
Lecture6_2015.pdf6.71MB
lecture7.ogv330.15MB
Lecture7_2015.pdf3.75MB
lecture8.ogv352.06MB
Lecture8_2015.pdf10.97MB
lecture9.ogv345.42MB
Lecture9_2015.pdf9.64MB
Type: Course
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Bibtex:
@article{,
title= {Large Scale Machine Learning - UToronto - STA 4273H Winter 2015},
keywords= {},
journal= {},
author= {},
year= {2015},
url= {http://www.cs.toronto.edu/~rsalakhu/STA4273_2015/lectures.html},
license= {},
abstract= {Lecture 1 -- Machine Learning:
Introduction to Machine Learning, Linear Models for Regression
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.
Reading: Bishop, Chapter 3: sec. 3.3 - 3.5. Chapter 4. 
Optional: Radford Neal's NIPS tutorial on Bayesian Methods for Machine Learning:. Also see Max Welling's notes on Fisher Linear Discriminant Analysis

Lecture 3 -- Classification 
Linear Models for Classification, Generative and Discriminative approaches, Laplace Approximation.
Reading: Bishop, Chapter 4. 
Optional: Hastie, Tibshirani, Friedman, Chapter 4. 

Lecture 4 -- Graphical Models: 
Bayesian Networks, Markov Random Fields
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

Lecture 5 -- Mixture Models and EM: 
Mixture of Gaussians, Generalized EM, Variational Bound.
Reading: Bishop, Chapter 9. 
Optional: Hastie, Tibshirani, Friedman, Chapter 13 (Prototype Methods). 
MacKay, Chapter 22 (Maximum Likelihood and Clustering).

Lecture 6 -- Variational Inference 
Mean-Field, Bayesian Mixture models, Variational Bound.
Reading: Bishop, Chapter 10. 
Optional: MacKay, Chapter 33 (Variational Inference).

Lecture 7 - Sampling Methods 
Rejection Sampling, Importance sampling, M-H and Gibbs.
Reading: Bishop, Chapter 11. 
Optional: MacKay, Chapter 29 (Monte Carlo Methods).

Lecture 8 -- Continuous Latent Variable Models 
PCA, FA, ICA, Deep Autoencders 
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.
Reading: Bishop, Chapter 13. },
superseded= {},
terms= {}
}