The schedule below represents my current best estimate concerning due dates (and everything else). I am providing this information to give you a general idea of the pace and timing of the class. This schedule will certainly change as the semester progresses.
Unless otherwise noted, all readings are from
WEEK | DATE | TOPIC | READING/VIDEOS | OUT | IN |
1 | 08-21 | Introduction | CH 1 through "Types of Machine Learning Systems" | Python Setup | |
2 | 08-26 | Decision Tree Activity (.pdf) Numpy Lab |
CH 6 Fast Numpy Video https://scipy-lectures.org/ (1.3.1-1.3.2) |
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08-28 | Decision Trees + Numpy | PA1 | |||
3 | 09-02 | Bias/Variance Trade-Off | Expected Value Bias/Variance |
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09-04 | Testing and Validation | CH 1 "Main Challenges of Machine Learning" to end Cross Validation (3.1) Leave One Out |
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4 | 09-09 | Evaluating Classifiers | CH 3 through "Error Analysis" | ||
09-11 | EXAM 1 | ||||
5 | 09-16 | Random Forest Boosting |
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09-18 | Probability | PA1 | |||
6 | 09-23 | Naive Bayes | |||
09-25 | Gradient Descent |
Linear Algebra Review (S) Video (S) More Videos (S) Calculus Refresher (S) 9-14 Neural Net Video 1/2 Neural Net Video 2/2 |
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7 | 09-30 | Logistic Regression | |||
10-02 | MLP | PyTorch Tensors PyTorch Autograd |
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8 | 10-07 | EXAM 2 | |||
10-09 | PyTorch Practice | PyTorch Tutorial | Poster Project | ||
9 | 10-14 | Regularization | deeplearningbook.org 7 - 7.1.2, 7.4 7.12 (s) 8.2.2 - 8.2.4, 8.3.2 |
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10-16 | Autodiff | Autodiff Video | |||
10 | 10-21 | FALL BREAK | |||
10-23 | Convnets | https://setosa.io/ev/image-kernels/ CNN Intro 1/3 CNN Intro 2/3 CNN Intro 3/3 (s) |
Poster Proposal | ||
11 | 10-28 | RNN |
Word Embedding Tutorial "Unreasonable Effectiveness" deeplearningbook.org 10-10.2.0 (s) |
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10-30 | RNN | LSTM Tutorial Transformer Tutorial |
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12 | 11-04 | EXAM 3 | |||
11-06 | Transformers | ThreeBlueOneBrown 5 ThreeBlueOneBrown 6 ThreeBlueOneBrown 7 |
Poster Bibliography | ||
13 | 11-11 | Transformers | |||
11-13 | SVM |
CH 4.9 | |||
14 | 11-18 | PCA |
Covariance Tutorial Video |
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11-20 | Nonlinear Dimensionality Reduction t-SNE Paper |
PDSH: Manifold Learning | Poster Draft | ||
15 | 11-25 | THANKSGIVING | |||
11-27 | THANKSGIVING | ||||
16 | 12-02 | Clustering | |||
12-04 | EXAM 4 |