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Instructor:
Dan Gildea office hours M/W 1-2pm, 3019 Wegmans
TAs:
Location: Tu/Th 11:05-12:20am, Gavett 202
Prereqs: Probability, Linear Algebra, Vector Calculus.
Homeworks
Lecture notes
Required text: Christopher M. Bishop, Pattern Recognition and Machine Learning.
The following are useful references in addition
to the reading material assigned for each class:
- Stuart Russell and Peter Norvig, Artificial Intelligence, A Modern Approach.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction.
- Larry Wasserman, All of Statistics, 2004.
Syllabus
| On | we will cover | which means that after class you will understand | if before class you have read |
|---|
| 1/13 |
Regression and Classification |
constrained optimization, perceptron
| bishop 1.2, 1.4, app E |
| 1/18 |
Logistic Regression |
stochastic gradient descent
| bishop 4.3 |
| 1/20 |
Backpropagation |
DP for gradient descent
| bishop 5.1, 5.2, 5.3 |
| 1/25 |
Deep Learning |
drop-out
| Krizhevsky 2012 |
| 1/27 |
Support Vectors |
max-margin
| bishop 3.1, 4.1 |
| 2/1 |
Support Vectors |
strong duality
| bishop 7.1 |
| 2/3 |
Support Vectors |
the kernel trick
| bishop 6.1, 6.2 |
| 2/8 |
Hidden Markov Models |
forward-backward
| bishop 13.2 |
| 2/10 |
Graphical Models |
bayes ball
| |
| 2/15 |
Probabilistic Inference |
message passing
| bishop 8.4 |
| 2/17 |
Tree decomposition |
cyclic graphs
| koller and friedman |
| 2/22 |
Tree decomposition cont'd |
vertex elimination
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| 2/24 |
Review |
| |
| 3/1 |
Midterm |
| |
| 3/3 |
Midterm Solutions |
| |
| 3/15 |
Fairness in machine learning |
| Kleinberg, Corbett-Davies |
| 3/17 |
Expectation Maximization |
L = Q + H + D
| bishop 9 |
| 3/22 |
Expectation Maximization |
mixture of gaussians
| bishop 9 |
| 3/24 |
EM for HMM |
minimum bayes risk
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| 3/29 |
Sampling |
Markov Chain Monte Carlo
| bishop 11.2 |
| 3/31 |
Metropolis Hastings |
detailed balance
| bishop 11.2 |
| 4/5 |
Gibbs sampling |
annealing
| bishop 11.3 |
| 4/7 |
Learning Theory |
PAC
| Kearns and Vazirani |
| 4/12 |
Gradient Descent |
SGD convergence
| Ruder 2016 |
| 4/14 |
GAN/VAE |
deep generative models
| Goodfellow et al. 2014, Kingma and Welling 2014 |
| 4/19 |
Reinforcement Learning |
q-learning
| sutton ch 3, 4.3, 4.4, 6.1, 6.5, 7.2, 11.1 |
| 4/21 |
Something fun |
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| 4/26 |
Review |
come to class with questions!
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Final exam: Friday May 6, 8:30-11:30am
Grading
- Homeworks: 50%
- Final exam: 30%
- Midterm: 20%
gildea @ cs rochester edu
April 13, 2022
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