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start [2024/07/22 15:05] – [November] anderson | start [2024/09/26 21:12] (current) – external edit 127.0.0.1 | ||
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The following schedule is **tentative and is being updated**. | The following schedule is **tentative and is being updated**. | ||
+ | All students may attend the lecture remotely using [[https:// | ||
===== August ===== | ===== August ===== | ||
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|< 100% 18% 20% 22% 20% 20% >| | |< 100% 18% 20% 22% 20% 20% >| | ||
^ Week ^ Topic ^ Lecture Notes ^ Reading | ^ Week ^ Topic ^ Lecture Notes ^ Reading | ||
- | | Week 1:\\ Aug 20, 22 | Course overview. | + | | Week 1:\\ Aug 20, 22 | Course overview. |
- | | Week 2:\\ Aug 27, 29 | Jupyter notebook animations. | + | | Week 2:\\ Aug 27, 29 | Optimization algorithms. Simple linear and nonlinear models. Confidence intervals. |
===== September ===== | ===== September ===== | ||
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|< 100% 18% 20% 22% 20% 20% >| | |< 100% 18% 20% 22% 20% 20% >| | ||
^ Week ^ Topic ^ Lecture Notes ^ Reading | ^ Week ^ Topic ^ Lecture Notes ^ Reading | ||
- | | Week 3:\\ Sept 3, 5\\ Chuck' | + | | Week 3:\\ Sept 3, 5 | Introduction to neural networks. |
- | | Week 4:\\ Sept 10, 12 | Design of NeuralNetwork class. Optimizers. | + | | Week 4:\\ Sept 10, 12 | Design of NeuralNetwork class. Optimizers. Overview of A2. Memory organization for neural network parameters. Optimizers tailored for neural networks. | [[https:// |
- | | Week 5:\\ Sept 17, 19 | Using optimizers. | | + | | Week 5:\\ Sept 17, 19\\ Chuck' |
- | | Week 6:\\ Sept 24, 26 | Early stopping (new version of optimizers). A3. Introduction to classification. | + | | Week 6:\\ Sept 24, 26 | Classification with Logistic Regression. | [[https:// |
===== October ===== | ===== October ===== | ||
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|< 100% 18% 20% 22% 20% 20% >| | |< 100% 18% 20% 22% 20% 20% >| | ||
^ Week ^ Topic ^ Lecture Notes ^ Reading | ^ Week ^ Topic ^ Lecture Notes ^ Reading | ||
- | | Week 7:\\ Oct 1, 3 | Classification with QDA, LDA, and linear logistic regression. | + | | Week 7:\\ Oct 1, 3 | Classification with QDA, LDA, and linear logistic regression. |
| Week 8:\\ Oct 8, 10 | Classification with Nonlinear Logistic Regression. Introduction to Reinforcement Learning. | | Week 8:\\ Oct 8, 10 | Classification with Nonlinear Logistic Regression. Introduction to Reinforcement Learning. | ||
| Week 9:\\ Oct 15, 17 | Reinforcement learning with Q Function as Neural Network. Learning to play games. | | [[https:// | | Week 9:\\ Oct 15, 17 | Reinforcement learning with Q Function as Neural Network. Learning to play games. | | [[https:// | ||
| Week 10:\\ Oct 22, 24 | Modular framework for reinforcement learning. Convolutional Neural Networks. | | Week 10:\\ Oct 22, 24 | Modular framework for reinforcement learning. Convolutional Neural Networks. | ||
- | | Week 11:\\ Oct 29, 31 | Ray. Pytorch. | + | | Week 11:\\ Oct 29, 31 | Pytorch.\\ Jax.\\ Ray. | | [[https:// |
===== November ===== | ===== November ===== | ||
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|< 100% 18% 20% 22% 20% 20% >| | |< 100% 18% 20% 22% 20% 20% >| | ||
^ Week ^ Topic ^ Lecture Notes ^ Reading | ^ Week ^ Topic ^ Lecture Notes ^ Reading | ||
- | | Week 12:\\ Nov 5, 7 | Convolutional Neural Networks. | + | | Week 12:\\ Nov 5, 7 | Convolutional Neural Networks. |
| Week 13:\\ Nov 12, 14 | Ensembles. Mixture of Experts. | | Week 13:\\ Nov 12, 14 | Ensembles. Mixture of Experts. | ||
- | | Week 14:\\ Nov 19, 21 | Clustering. K-Nearest Neighbors. Jax. Web Apps with Streamlit. | + | | Week 14:\\ Nov 19, 21 | Clustering. K-Nearest Neighbors. Web Apps with Streamlit. |
| Fall Break:\\ Nov 25-29 | No classes. | | Fall Break:\\ Nov 25-29 | No classes. | ||
start.1721682313.txt.gz · Last modified: 2024/07/22 15:05 by anderson