STA 4273H (Fall 2013): Statistical Machine Learning
Tuesdays 2:00pm to 5:00pm in LM123
Ruslan (Russ) Salakhutdinov, Office: SS6002,
rsalakhu [at] utstat [dot] toronto [dot] edu
- Lectures: Tuesdays 2:00pm to 5:00pm in LM123
- First Lecture: : Sep 10, 2013.
- Office hours: Tuesdays 12-1pm.
Statistical machine learning is a very dynamic field that lies at the
intersection of Statistics and computational sciences. The goal of
statistical machine learning is to develop algorithms that can "learn"
from data using statistical and computational methods. Over the last
decade, numerous research fields, such as computational biology,
neuroscience, artificial intelligence, data mining, signal processing,
finance have been strongly influenced by advances in machine learning.
This is an advanced graduate course, designed for Master's and Ph.D. level
students, and will assume a reasonable degree of mathematical maturity.
Specific topics to be covered include:
- Linear methods for
- Model assessment and selection
models, Bayesian networks, Markov random fields, conditional random
variational inference, mean-field inference
sampling algorithms, Markov chain Monte Carlo,
Gibbs sampling, and Metropolis-Hastings algorithm
- Mixture models and generalized mixture models
- Unsupervised learning, probabilistic
PCA, factor analysis, independent component analysis.
Prerequisite : Knowledge of statistical inference, probability theory, and
linear algebra at the advanced undergraduate level, and some basic
programming skills in R or Matlab. STA414/2104 is a plus, but is not
Email: rsalakhu [at] utstat [dot] toronto [dot] edu
Office: Sidney Smith Hall, Room 6002
Course Information |
Lecture Schedule/Notes |
STA 4273H (Fall 2013): Research Topics In Statistical Machine Learning