Link back to the Syllabus
Schedule from 2017</a>
| Week | Date | Topic | Readings |
|---|---|---|---|
| 1 | September 5, 2017 | Introduction to Machine Learning: Lec1-Introduction.pdf | Assignment #1 released with associated code simulate.py and tex file to make it simpler to typeset your solutions (if you choose to do so). Thought questions #1: - read Chapters 1, 2, 3 and 4 from the notes.pdf |
| 1 | September 7, 2017 | Introduction to Probability: Lec2-Probability.pdf | Great blog post if you want additional readings about probability; jump to the section on Rigorous Foundations, unless you would like to learn about bandits (which is also fun!) |
| 2 | September 12, 2017 | Introduction to Probability (cont): Lec2-Probability.pdf Start parameter estimation: Lec3-ParameterEstimation.pdf |
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| 2 | September 14, 2017 | Parameter estimation: Lec3-ParameterEstimation.pdf | |
| 3 | September 19, 2017 | Introduction to Prediction Problems: Lec5-IntroPrediction.pdf | Sept. 18 last day to add or drop courses. |
| 3 | September 21, 2017 | Linear Regression: Lec6-LinearRegression.pdf | Thought questions #1 due (Thursday) Thought questions #2: - read Chapters 5, 6, and 7 from the notes.pdf |
| 4 | September 26, 2017 | Linear Regression (cont): Lec6-LinearRegression.pdf | |
| 4 | September 28, 2017 | Linear Regression: Regularization and the bias-variance trade-off (mostly done on the board, but some information in Lec6-LinearRegression.pdf) | Assignment #1 due (Thursday) Assignment #2 released with associated code a2barebones.zip and tex file |
| 5 | October 3, 2017 | More advanced optimization: Lec9-Optimization.pdf | Useful additional notes from Duchi and Singer |
| 5 | October 5, 2017 | Finish off optimization Start Generalized linear models |
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| 6 | October 10, 2017 | Generalized linear models: Lec11-GLMs.pdf | Martha’s Office hours moved to 4 p.m. on Tuesday (instead of 3 p.m.) |
| 6 | October 12, 2017 | Evaluating learning algorithms: Lec12-EvaluationBasics.pdf | Thought questions #2 due (Thursday) Thought questions #3: - read Chapters 8, 9, and 10 from the notes.pdf |
| 7 | October 17, 2017 | Logistic regression: Lec13-LogisticRegression.pdf | Demo with outlier |
| 7 | October 19, 2017 | Multi-class classification and multinomial logistic regression: Lec14-Multiclass.pdf | |
| 8 | October 24, 2017 | Naive Bayes and generative models: Lec15-Generative.pdf | |
| 8 | October 26, 2017 | Fixed representations: Lec16-Representations.pdf | Assignment #2 due (Thursday) Assignment #3 released with associated code a3barebones.zip and tex file |
| 9 | October 31, 2017 | Neural networks: Lec17-NeuralNetworks.pdf | |
| 9 | November 2, 2017 | Neural networks (cont.): Lec17-NeuralNetworks.pdf | Thought questions #3 due (Saturday) |
| 10 | November 7, 2017 | Sparse coding and dimensionality reduction: Lec19-Factorization.pdf | |
| 10 | November 9, 2017 | More about empirical evaluation: Lec20-MeasuringPerformance.pdf | |
| 11 | November 14, 2017 | No classes: Reading week | |
| 11 | November 16, 2017 | No classes: Reading week | |
| 12 | November 21, 2017 | Embedding models and metric learning: Lec21-Embeddings.pdf | |
| 12 | November 23, 2017 | Hidden variables: Lec22-HiddenVariables.pdf | Assignment #3 due (Friday) |
| 13 | November 28, 2017 | Boosting: Lec23-Ensembles.pdf | Draft of Mini-project due Tuesday (15% of mark for undergrads, 5% of mark for grads) |
| 13 | November 30, 2017 | More advanced neural networks: Lec24-NNArchitectures.pdf | December 1 last day for withdrawal. Feedback by grads on mini-projects due by Friday (10% of mark for grads) |
| 14 | December 5, 2017 | Bayesian linear regression: Lec25-BayesianApproach.pdf | |
| 14 | December 7, 2017 | Review class: Lec26-Review.pdf | Final Mini-project due (10% of mark, Friday) |
| Final | Friday, December 15, 2017, 2:00 p.m. in ETLC E1 013 | Final Exam | You can bring a two page cheat-sheet. |