HOML = Hands-On Machine Learning, 2nd Edition
| Class # | Date | Topic | Reading | Assignment | Due |
| 1 | Tue, Aug 20 | Class Introduction Taxonomy of Machine Learning Methods | Syllabus, HOML CH 1 | ||
| 2 | Thu, Aug 22 | Computing Environment Representing Data I: Pandas and Numpy | HOML CH 2 Python for Programmers: see Canvas links Python fundamentals (for those needing more of an introduction): Python Tutorials | ||
| 3 | Tue, Aug 27 | Representing Data II: Statistics and Visualization | continued | ||
| 4 | Thu, Aug 29 | Representing Data III: Pipelines and Transformations | cont | HW A | |
| 5 | Tue, Sep 3 | 1D Convolution | See Discrete Convolution | ||
| 6 | Thu, Sep 5 | Classifiers I | HOML CH 3 | HW B | HW A |
| 7 | Tue, Sep 10 | Classifiers II | HOML CH 3 cont | ||
| 8 | Thu, Sep 12 | Classifiers III | n/a | HW C | HW B |
| 9 | Tue, Sep 17 | Linear Regression I | HOML CH 4 (pp. 111-117) | ||
| 10 | Thu, Sep 19 | Linear Regression II: Gradient Methods | cont pp. 117-128 | HW D | HW C |
| 11 | Tue, Sep 24 | Polynomial Regression | cont pp. 128-133 | ||
| 12 | Thu, Sep 26 | Overfitting and Regularization | cont pp. 133-142 | HW E | HW D |
| 13 | Tue, Oct 1 | Cross-Validation | Splitting data sets (focus on the high-level view) | ||
| 14 | Thu, Oct 3 | Cross-Validation II | n/a | HW F | HW E |
| 15 | Tue, Oct 8 | Hypothesis Testing | n/a | ||
| 16 | Thu, Oct 10 | Hyperparameter Selection | Hyper-parameter Tuning (Jeremy Jordan) | HW G | HW F |
| 17 | Tue, Oct 15 | Formally Comparing Models | Statistical Significance Tests for Comparing Machine Learning Algorithms (Jason Brownlee) | ||
| 18 | Thu, Oct 17 | Decision Trees: Basics | HOML CH 6 | HW H | HW G |
| 19 | Tue, Oct 22 | No class | n/a | ||
| 20 | Thu, Oct 24 | Decision Trees: Regression | cont | ||
| 21 | Tue, Oct 29 | Decision Trees: Ensemble Methods | HOML CH 7 | HW I | HW H |
| 22 | Thu, Oct 31 | Decision Trees: Random Forests | cont | ||
| 23 | Tue, Nov 5 | Decision Trees: Boosting | cont | ||
| 24 | Thu, Nov 7 | Principal Component Analysis Kernel PCA | HOML CH 8 | HW J | HW I |
| 25 | Tue, Nov 12 | Local Linear Embedding | cont | ||
| 26 | Thu, Nov 14 | Multidimensional Scaling | cont | ||
| 27 | Tue, Nov 19 | ISOmap, t-SNE, Umap | cont | HW K | HW J |
| 28 | Thu, Nov 21 | Unsupervised Learning: K-Means Clustering | HOML CH 9 (pp. 235-252) | ||
| 29 | Tue, Nov 26 | Clustering: Gaussian Mixture Models | HOML CH 9 (pp. 260-275) | ||
| - | - | Thanksgiving Holiday | |||
| 30 | Tue, Dec 3 | Semi-Supervised Learning | HOML CH 9 (pp. 253-260) | HW K | |
| 31 | Thu, Dec 5 | Semester Wrap-Up | n/a | ||
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