| 1 |
8/19 - 8/23 |
Mathematical Building Blocks of Machine Learning |
https://youtu.be/xx9vDj2_A1c |
| 2 |
8/26 - 8/30 |
Python Programming for Machine Learning, Part 1: Object-Oriented Programming |
https://youtu.be/iTtp5BBi-w0 |
| 3 |
9/3 - 9/6 |
Python Programming for Machine Learning, Part 2: Tensor, Gradient, and Automatic Differentiation |
https://youtu.be/sOY57NJ0LMs |
| 4 |
9/9 - 9/13 |
Loss Function Derivation for Regression Problems |
https://youtu.be/MXi8liTFquM |
| 5 |
9/16 - 9/20 |
Loss Function Optimization using Gradient Descent |
https://youtu.be/0hzFtjHTsYs |
| 6 |
9/23 - 9/27 |
Logistic Regression and Loss Function Derivation for Classification Problems |
https://youtu.be/QZ_qNldu2eo https://youtu.be/eo69GAVT-T4 |
| 7 |
9/30 - 10/4 |
Evaluating Classification Models |
https://youtu.be/K5HP78pmJVs https://youtu.be/NCZp7QtXXY0 |
| 8 |
10/7 - 10/11 |
Mathematical Building Blocks of Neural Networks |
https://youtu.be/3pFDgR245HU |
| 9 |
10/14 - 10/18 |
Implementing Feedforward Neural Networks using Keras and Backpropagation |
https://youtu.be/cXTHzXXuZlc |
| 10 |
10/21 - 10/25 |
Neural Networks in Action |
https://youtu.be/Bru7lf-KhkM |
| 11 |
10/28 - 11/1 |
Implementing Convolutional Neural Networks using Keras |
https://youtu.be/4K6mpCRvO74 |
| 12 |
11/4 - 11/8 |
Hyperparameter Optimization for Neural Networks using KerasTuner |
https://youtu.be/pC5EsO92R4M |
| 13 |
11/11 - 11/15 |
Beyond Point Predictions: Building Prediction Sets with Conformal Inference |
https://youtu.be/oqK6rM8fbkk |
| 14 |
11/18 - 11/22 |
Implementing Recurrent Neural Networks using Keras |
https://youtu.be/uOnnCOF11NY |
| 15 |
12/2 - 12/6 |
Final Project: Presentation, Analysis, and Peer Feedback |
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