Link back to the Syllabus
All readings are from the (in progress) machine learning notes. These are designed to be short, so that you can read every chapter. The sections labeled Advanced can be skipped. I recommend avoiding printing these notes, since later parts of the notes are likely to be modified (even if only a little bit).
This schedule is tentative, and is likely to change throughout the semester. The links are what will tentatively be in the schedule, and in some cases do not yet link to the material.
Link to schedule from 2017
Link to schedule from 2018
| Week | Date | Topic | Readings |
|---|---|---|---|
| 1 | September 3, 2018 | 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 the Preface and Chapters 1, 2, 3 and 4 from the notes.pdf |
| 1 | September 5, 2019 | 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 10, 2019 | Introduction to Probability (cont): Lec2-Probability.pdf Start parameter estimation: Lec3-ParameterEstimation.pdf |
|
| 2 | September 12, 2019 | Parameter estimation: Lec3-ParameterEstimation.pdf | |
| 3 | September 17, 2019 | Introduction to Prediction Problems: Lec5-IntroPrediction.pdf | Sept. 16 last day to add or drop courses. |
| 3 | September 19, 2019 | Continued Introduction to Prediction Problems, wrote on board: Lec5-IntroPrediction.pdf</a | Thought questions #1 due (Thursday) Thought questions #2: - read Chapters 5, 6, and 7 from the notes.pdf |
| 4 | September 24, 2019 | Linear Regression: Lec6-LinearRegression.pdf | |
| 4 | September 26, 2019 | 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 1, 2019 | More about bias-variance | |
| 5 | October 3, 2019 | More advanced optimization: Lec9-Optimization.pdf | Useful additional notes from Duchi and Singer, called “Proximal and First-Order Methods for Convex Optimization” |
| 6 | October 8, 2019 | More on optimization | Demo with outlier |
| 6 | October 10, 2019 | Finish optimization | Thought questions #2 due (Thursday) Thought questions #3: - read Chapters 8, 9, and 10 from the notes.pdf |
| 7 | October 15, 2019 | start Generalized linear models and logistic regression: Lec11-GLMs.pdf | More about empirical evaluation: Lec20-MeasuringPerformance.pdf |
| 7 | October 17, 2019 | Evaluating learning algorithms: Lec12-EvaluationBasics.pdf | |
| 8 | October 22, 2019 | Multi-class classification and multinomial logistic regression: Lec14-Multiclass.pdf | |
| 8 | October 24, 2019 | Naive Bayes and generative models: Lec15-Generative.pdf | Assignment #2 due (Thursday) Assignment #3 released with associated code a3barebones.zip and tex file |
| 9 | October 29, 2019 | Finish Naive Bayes and start Fixed representations: Lec16-Representations.pdf | |
| 9 | October 31, 2019 | Continue Fixed representations and intro to NNs: Lec16-Representations.pdf with slides about computing the gradient Lec17-ComputeNNGradient.pdf | |
| 10 | November 5, 2019 | Midterm Review: Lec18-MidReview | Office hours moved to Tuesday, 2-4 p.m. |
| 10 | November 7, 2019 | Midterm (NOT IN HC L1!) | Go to CCIS 1 440 |
| 11 | November 12, 2019 | No classes: Reading week | |
| 11 | November 14, 2019 | No classes: Reading week | Thought questions #3 due |
| 12 | November 19, 2019 | Measuring performance: Lec20-MeasuringPerformance.pdf | |
| 12 | November 21, 2019 | More about Neural networks: Lec17-NeuralNetworks.pdf | Assignment #3 due (Friday) |
| 13 | November 26, 2019 | Embedding models Lec21-FactorizationAndEmbeddings.pdf , Theoretical Analysis and Generalization Error | Initial Draft of Mini-project due Tuesday (5% of your mark) Office hours moved to Thursday, from 2-4 p.m. |
| 13 | November 28, 2019 | More advanced neural networks Lec24-NNArchitectures.pdf | November 29 last day for withdrawal. |
| 14 | December 3, 2019 | Yangchen Pan on Neural Network Architectures, Martha on Generative models and VAEs | |
| 14 | December 5, 2019 | Review class: Lec26-Review.pdf, with Andrew Patterson | Final Mini-project due Friday (5% of mark) Mini-project (optional) bonus due Friday |
| Final | Tuesday, December 17, 2019, 9:00 a.m. | Final Exam | You can bring a two page cheat-sheet. |