CS B555: Machine Learning

Class schedule

This schedule is tentative and may change throughout the semester.

Readings are from the notes: notes.pdf

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

An appendix (with additional optimization information) will be updated progressively: appendix.pdf

The references for the document are: references.pdf

These notes are released progressively; look at the date on the first page to see how recently they have been updated.

Week	Date	Lecture	Readings and Deadlines
1	Aug 24	Introduction: Lec1-Introduction.pdf	Assignment #1 released Thought questions #1: -read Chapters 1 and 2 from the notes.pdf
	Aug 26	Probability: Lec2-Probability.pdf
2	Aug 31	Probability: Lec3-Probability.pdf
	Sep 2	Parameter Estimation: Lec4-ParameterEstimation.pdf
3	Sep 7	No classes: Labour day Office hours moved to Wednesday
	Sep 9	Finish parameter estimation and introduce prediction problems: Lec5-IntroML.pdf Martha's office hours from 2-4 p.m.	Thought questions #1 due
4	Sep 14	Finish intro to prediction problems: Lec6-Prediction.pdf
	Sep 16	Linear regression: Lec7-LinearRegression.pdf	Assignment #1 due Assignment #2 released
5	Sep 21	Linear regression (cont...): Lec8-LinearRegression2.pdf Matlab demo: example11.m The issue in class was that the features were also not centered; both the features and the target have to be centered (mean removed) (or a column of 1s needs to be added).
	Sep 23	Practical linear regression: Lec9-PracticalLinearRegression.pdf
6	Sep 28	Stochastic optimization: Lec10-StochasticOptimization.pdf
	Sep 30	Generalized Linear Models: Lec11-GLMs.pdf	Thought questions #2 due
7	Oct 5	GLMs and Logistic regression: Lec12-LogisticRegression.pdf
	Oct 7	Naive Bayes: Lec13-NaiveBayes.pdf Matlab demo: logistic regression and linear regression, logistic.zip
8	Oct 12	Multiclass Classification: Lec14-Multiclass.pdf In-class questions: feedback.pdf
	Oct 14	Representation learning: Lec15-Representations.pdf	Assignment #2 due Assignment #3 released
9	Oct 19	Neural networks:Lec16-NeuralNetworks.pdf	Class project released
	Oct 21	Evaluation basics: Lec17-EvaluationBasics.pdf	Thought questions #3 due
10	Oct 26	Neural networks and factorization: Lec18-NeuralNetsandFactorizationIntro.pdf
	Oct 28	Factorization: Lec19-Factorization.pdf
11	Nov 2	Factorization (cont...): Lec20-Factorization2.pdf
	Nov 4	SVMs: Lec21-SVMs.pdf
12	Nov 9	Semi-supervised learning and missing data: Lec22-Semisupervised.pdf
	Nov 11	Hidden variables models: Lec23-HiddenVariables.pdf EM demo: em.zip
13	Nov 16	Mixture models: Lec24-MixtureModels.pdf Additional notes: notes_mixtures.pdf	Thought questions #4 due
	Nov 18	Bayesian estimation: Lec25-BayesianApproach.pdf Updated notes to include sections on mixture models (and EM approach) and Bayesian learning.	Assignment #3 due Assignment #4 released
14	Nov 23	No classes: Thanksgiving
	Nov 25	No classes: Thanksgiving
15	Nov 30	Ensemble learning: Lec26-Ensembles.pdf
	Dec 2	Performance measures: Lec27-ErrorFunctions.pdf
16	Dec 7	Course review: Lec28-Review.pdf Office hours canceled for Martha Office hours still on for Shantanu
	Dec 9	No class (away at conference) Office hours still on for Zeeshan	Assignment #4 due Class project due (Friday)
17	Dec 14	Additional review class from 11:00 a.m. to 12:15 p.m. in Info East 130, on Monday
	Dec 16	Final exam: 5:00 p.m. - 7:00 p.m., Info East 130