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The following schedule is tentative and is being updated.
August
| Week | Topic | Lecture Notes | Reading | Assignments |
|---|---|---|---|---|
| Week 1: Aug 22, 24 | Course overview. Jupyter notebooks. | 01 Introduction to CS545 02 Searching for Good Weights in a Linear Model | JupyterLab Introduction, watch the video then play with jupyter lab. The Batch from DeepLearning.AI. Yay, Colorado! What is Data Analysis? How to Visualize Data with Python, Numpy, Pandas, Matplotlib & Seaborn Tutorial, by Aakash NS | Not graded: Please fill out this anonymous survey before Thursday class. |
| Week 2: Aug 29, 31 | Jupyter notebook animations. Optimization algorithms. Simple linear and nonlinear models. | 01a Simple Animations 02 Searching for Good Weights in a Linear Model 02a Generative AI--Friend or Foe 03 Searching for Good Weights in a Linear Model |
September
| Week | Topic | Lecture Notes | Reading | Assignments |
|---|---|---|---|---|
| Week 3: Sept 5, 7 Chuck's office hours Thursday will be from 2 to 3:30. | Confidence intervals. Introduction to neural networks. | 04 Training Multiple Models to Obtain Confidence Intervals 05 Introduction to Neural Networks | A1 due Friday, September 8th, 10:00 PM | |
| Week 4: Sept 12, 14 | Design of NeuralNetwork class. Optimizers. | Weight Initialization for Deep Learning Neural Networks, by Jason Brownlee | ||
| Week 5: Sept 19, 21 | Introduction to classification. | A2 NeuralNetwork Class due Wednesday, September 20th, 10:00 PM | ||
| Week 6: Sept 26, 28 | Classification. Convolutional neural networks. |
October
| Week | Topic | Lecture Notes | Reading | Assignments |
|---|---|---|---|---|
| Week 7: Oct 3, 5 | Introduction to Jax and Pytorch. | |||
| Week 8: Oct 10, 12 | More convolutional neural networks. | |||
| Week 9: Oct 17, 19 | Introduction to reinforcement leanring. Learning to play games. | |||
| Week 10: Oct 24, 26 | Reinforcement learning for control of dynamic systems. |
November
| Week | Topic | Lecture Notes | Reading | Assignments |
|---|---|---|---|---|
| Week 11: Oct 31 Nov 2 | Recurrent neural networks. | |||
| Week 12: Nov 7, 9 | Unsupervised learning. Dimensionality reduction. Autoencorders. | |||
| Week 13: Nov 14, 16 | Clustering. | |||
| Fall Break: Nov 20-24 | No classes | |||
| Week 14: Nov 28, 30 | Ensemble methods. Mixture-of-experts. Transformers. |
December
| Week | Topic | Lecture Notes | Reading | Assignments |
|---|---|---|---|---|
| Week 15: Dec 5, 7 | Other topics in current research. | AI Scientists’ Perspectives on AI | ||
| Dec 11-15 | Final Exam Week | No Exams in this course |
start.1694532824.txt.gz · Last modified: 2023/09/12 09:33 by anderson