HOML = Hands-On Machine Learning, 2nd Edition
| Class # | Date | Topic | Reading | Assignment | Due |
| 1 | Tue, Aug 22 | Class Introduction Taxonomy of Machine Learning Methods | Syllabus | ||
| 2 | Thu, Aug 24 | Computing Environment and Python Basics | HOML CH 1 | ||
| 3 | Tue, Aug 29 | Python Basics II | Python Tutorials (for those needing to come up to speed) | HW0 | |
| 4 | Thu, Aug 31 | Representing Data I: Pandas and Numpy | HOML CH 2 | ||
| 5 | Tue, Sep 5 | Representing Data II: Statistics and Visualization | continued | HW 1 | HW0 |
| 6 | Thu, Sep 7 | Representing Data III: Pipelines and Transformations Implementing Convolutional Operators | continued | ||
| 7 | Tue, Sep 12 | Classifiers I | HOML CH 3 | HW 2 | HW 1 |
| 8 | Thu, Sep 14 | Classifiers II | continued | ||
| 9 | Tue, Sep 19 | Feature Importance | continued | HW3 | HW 2 |
| 10 | Thu, Sep 21 | Linear Regression I | HOML CH 4 (pp. 111-117) | ||
| 11 | Tue, Sep 26 | Linear Regression II: Gradient Methods | cont pp. 117-128 | HW 4 | HW3 |
| 12 | Thu, Sep 28 | Polynomial Regression | cont pp. 128-133 | ||
| 13 | Tue, Oct 3 | Overfitting and Regularization | cont pp. 133-142 | HW 5 | HW4 |
| 14 | Thu, Oct 5 | Cross-Validation | Splitting data sets (focus on the high-level view) | ||
| 15 | Tue, Oct 10 | Hyperparameter Selection | Hyper-parameter Tuning (Jeremy Jordan) | HW 6 | HW 5 |
| 16 | Thu, Oct 12 | Formally Comparing Models | Statistical Significance Tests for Comparing Machine Learning Algorithms (Jason Brownlee) | ||
| 17 | Tue, Oct 17 | Logistic Regression | HOML CH4, pp. 142-151 | ||
| 18 | Thu, Oct 19 | Support Vector Machines | HOML CH 5 | HW 7 | HW 6 |
| 19 | Tue, Oct 24 | Support Vector Machines | continued | ||
| 20 | Thu, Oct 26 | Decision Trees: Basics | HOML CH 6 | ||
| 21 | Tue, Oct 31 | Decision Trees: Regression | continued | HW 8 | HW 7 |
| 22 | Thu, Nov 2 | Decision Trees: Ensemble Methods | HOML CH 7 | ||
| 23 | Tue, Nov 7 | Decision Trees: Random Forests | continued | HW 9 | HW 8 |
| 24 | Thu, Nov 9 | Decision Trees: Boosting | continued | ||
| 25 | Tue, Nov 14 | Principal Component Analysis Kernel PCA | HOML CH 8 | HW 10 | HW 9 |
| 26 | Thu, Nov 16 | Local Linear Embedding | continued | ||
| 27 | Tue, Nov 21 | Multidimensional Scaling | continued | HW 11 | HW 10 |
| - | - | Thanksgiving Holiday | |||
| 28 | Tue, Nov 28 | ISOmap and t-SNE | continued | ||
| 29 | Thu, Nov 30 | Unsupervised Learning: K-Means Clustering | HOML CH 9 (pp. 235-252) | HW 12 | HW 11 |
| 30 | Tue, Dec 5 | Clustering: Gaussian Mixture Models | HOML CH 9 (pp. 260-275) | ||
| 31 | Thu, Dec 7 | Semi-Supervised Learning | HOML CH 9 (pp. 253-260) | HW 12 | |
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