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CS B555: Machine Learning
Class schedule
This schedule is tentative and may change throughout the semester.
Readings are from the notes: notes.pdf Download notes.pdf
A (very rough and still in-progress) reference for math notation: notation.pdf Download notation.pdf
An appendix (with additional optimization information) will be updated progressively: appendix.pdf Download appendix.pdf
The references for the document are: references.pdf Download 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 Download Lec1-Introduction.pdf |
Assignment #1 released Thought questions #1: -read Chapters 1 and 2 from the notes.pdf Download notes.pdf |
| Aug 26 | Probability: Lec2-Probability.pdf Download Lec2-Probability.pdf | ||
| 2 | Aug 31 | Probability: Lec3-Probability.pdf Download Lec3-Probability.pdf | |
| Sep 2 | Parameter Estimation: Lec4-ParameterEstimation.pdf Download Lec4-ParameterEstimation.pdf | ||
| 3 | Sep 7 |
No classes: Labour day Office hours moved to Wednesday |
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| Sep 9 |
Finish parameter estimation and introduce prediction problems: Lec5-IntroML.pdf Download 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 Download Lec6-Prediction.pdf | |
| Sep 16 | Linear regression: Lec7-LinearRegression.pdf Download Lec7-LinearRegression.pdf |
Assignment #1 due Assignment #2 released |
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| 5 | Sep 21 |
Linear regression (cont...): Lec8-LinearRegression2.pdf Download Lec8-LinearRegression2.pdf Matlab demo: example11.m Download 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). |
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| Sep 23 | Practical linear regression: Lec9-PracticalLinearRegression.pdf Download Lec9-PracticalLinearRegression.pdf | ||
| 6 | Sep 28 | Stochastic optimization: Lec10-StochasticOptimization.pdf Download Lec10-StochasticOptimization.pdf | |
| Sep 30 | Generalized Linear Models: Lec11-GLMs.pdf Download Lec11-GLMs.pdf | Thought questions #2 due | |
| 7 | Oct 5 | GLMs and Logistic regression: Lec12-LogisticRegression.pdf Download Lec12-LogisticRegression.pdf | |
| Oct 7 |
Naive Bayes: Lec13-NaiveBayes.pdf Download Lec13-NaiveBayes.pdf Matlab demo: logistic regression and linear regression, logistic.zip Download logistic.zip |
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| 8 | Oct 12 |
Multiclass Classification: Lec14-Multiclass.pdf Download Lec14-Multiclass.pdf In-class questions: feedback.pdf Download feedback.pdf |
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| Oct 14 | Representation learning: Lec15-Representations.pdf Download Lec15-Representations.pdf |
Assignment #2 due Assignment #3 released |
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| 9 | Oct 19 | Neural networks:Lec16-NeuralNetworks.pdf Download Lec16-NeuralNetworks.pdf | Class project released |
| Oct 21 | Evaluation basics: Lec17-EvaluationBasics.pdf Download Lec17-EvaluationBasics.pdf | Thought questions #3 due | |
| 10 | Oct 26 | Neural networks and factorization: Lec18-NeuralNetsandFactorizationIntro.pdf Download Lec18-NeuralNetsandFactorizationIntro.pdf | |
| Oct 28 | Factorization: Lec19-Factorization.pdf Download Lec19-Factorization.pdf | ||
| 11 | Nov 2 | Factorization (cont...): Lec20-Factorization2.pdf Download Lec20-Factorization2.pdf | |
| Nov 4 | SVMs: Lec21-SVMs.pdf Download Lec21-SVMs.pdf | ||
| 12 | Nov 9 | Semi-supervised learning and missing data: Lec22-Semisupervised.pdf Download Lec22-Semisupervised.pdf | |
| Nov 11 |
Hidden variables models: Lec23-HiddenVariables.pdf Download Lec23-HiddenVariables.pdf EM demo: em.zip Download em.zip |
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| 13 | Nov 16 |
Mixture models: Lec24-MixtureModels.pdf Download Lec24-MixtureModels.pdf Additional notes: notes_mixtures.pdf Download notes_mixtures.pdf |
Thought questions #4 due |
| Nov 18 |
Bayesian estimation: Lec25-BayesianApproach.pdf Download Lec25-BayesianApproach.pdf Updated notes to include sections on mixture models (and EM approach) and Bayesian learning. |
Assignment #3 due Assignment #4 released
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| 14 | Nov 23 | No classes: Thanksgiving | |
| Nov 25 | No classes: Thanksgiving |
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| 15 | Nov 30 | Ensemble learning: Lec26-Ensembles.pdf Download Lec26-Ensembles.pdf | |
| Dec 2 | Performance measures: Lec27-ErrorFunctions.pdf Download Lec27-ErrorFunctions.pdf | ||
| 16 | Dec 7 |
Course review: Lec28-Review.pdf Download Lec28-Review.pdf Office hours canceled for Martha Office hours still on for Shantanu |
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| Dec 9 |
No class (away at conference) Office hours still on for Zeeshan |
Assignment #4 due Class project due (Friday) |
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| 17 | Dec 14 |
Additional review class from 11:00 a.m. to 12:15 p.m. in Info East 130, on Monday |
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| Dec 16 |
Final exam: 5:00 p.m. - 7:00 p.m., Info East 130 |