CS B555: Machine Learning

Class schedule

This schedule is tentative and will almost definitely change throughout the semester.

Readings are from the notes: notes.pdf

These notes may change to fix any issues or typos; look at the date on the first page to see how recently they have been updated. I recommend avoiding printing anything beyond the chapter you are currently reading, as some sections may change. This is particularly true for Chapter 6-8 and the appendix.

A (very rough and still in-progress) reference for math notation: notation.pdf

Week	Date	Lecture	Readings and Deadlines
1	Aug 22	Introduction: Lec1-Introduction.pdf	Assignment #1 released Thought questions #1: -read Chapters 1, 2 and 3 from the notes.pdf
	Aug 24	Probability: Lec2-Probability.pdf
2	Aug 29	Probability: Lec2-Probability-2.pdf Starting Parameter Estimation: Lec4-ParameterEstimation.pdf	Office hours for AIs: 2:30 - 4:00 p.m. Tuesday 1:00 - 2:30 p.m. Wednesday Office hours for Martha: 3:00 - 5:00 p.m. Tuesday
	Aug 31	Parameter Estimation: Lec4-ParameterEstimation_2.pdf
3	Sep 5	No classes: Labour day Office hours moved to Wednesday
	Sep 7	Intro to prediction problems: Lec5-IntroML.pdf	Thought questions #1 due
4	Sep 12	Linear regression: Lec7-LinearRegression.pdf
	Sep 14	Linear regression:Lec7-PracticalLinearRegression.pdf Matlab demo: example11.m	Assignment #1 due Assignment #2 released
5	Sep 19	Regularization: Lec8-Regularization.pdf
	Sep 21	Optimization: Lec9-StochasticOptimization.pdf
6	Sep 26	Generalized Linear Models: Lec10-GLMs.pdf
	Sep 28	Generalized Linear Models and logistic regression: Lec11-LogisticRegression.pdf Matlab demo: logistic regression and linear regression, logistic.zip	Thought questions #2 due
7	Oct 3	Naive Bayes: Lec12-NaiveBayes.pdf
	Oct 5	Multiclass classification: Lec13-Multiclass.pdf In-class questions: feedback.pdf
8	Oct 10	Multiclass Classification: Lec14-Multiclass2.pdf
	Oct 12	Multiclass Classification (cont): Lec14-Multiclass2.pdf	Assignment #2 due Assignment #3 released
9	Oct 17	SVMs: Lec15-SVMs.pdf
	Oct 19	Review: Lec16-Review.pdf	Thought questions #3 due
10	Oct 24	Fixed representations for learning nonlinear functions: Lec17-Representations.pdf
	Oct 26	Fixed representations (continued...): Lec18-Representations2.pdf
11	Oct 31	Neural networks: Lec19-NeuralNetworks.pdf
	Nov 2	Neural networks (cont...): Lec20-NeuralNetworks2.pdf	Quiz #1: Chapters 1-4
12	Nov 7	Factorization and unsupervised learning: Lec21-Factorization.pdf
	Nov 9	Semi-supervised learning, missing data and hidden variable models: Lec22-MissingVariables.pdf
13	Nov 14	Hidden variable models and Bayesian regression: Lec23-HiddenVariables.pdf EM demo: em.zip
	Nov 16	Boosting and ensembles: Lec24-Ensembles.pdf	Assignment #3 due Assignment #4 released
14	Nov 21	No classes: Thanksgiving
	Nov 23	No classes: Thanksgiving
15	Nov 28	Performance measures and evaluation: Lec27-EvaluationAndErrorFunctions.pdf Office hours for Martha: 5:30 p.m. - 7:00 p.m. on Monday
	Nov 30	Guest lecture: Prof. Donald Williamson	Quiz #2: Chapters 5-7
16	Dec 5	No class No office hours for Martha, but office hours for AIs remain the same
	Dec 7	No class	Assignment #4 due
17	Dec 14	Additional review class from 4:00 p.m. to 5:15 p.m. in Info East 150
	Dec 16	Final exam: 5:00 p.m. - 7:00 p.m., JHA 100

FA16: MACHINE LEARNING: 13974