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The following schedule is tentative and is being updated.
August
Week | Topic | Lecture Notes | Reading | Assignments |
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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 |
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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. | 06 Python Classes 07 Optimizers | Weight Initialization for Deep Learning Neural Networks, by Jason Brownlee | |
Week 5: Sept 19, 21 | Using optimizers. | 08 Collecting All Weights into One-Dimensional Vector for Use in Optimizers | A2 NeuralNetwork Class due Thursday, September 21st, 10:00 PM A2 and A2grader.zip updated Sept. 19, 10:45 AM |
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Week 6: Sept 26, 28 No on-campus lectures. Tuesday lecture pre-recorded and available on Echo360. Thursday lecture live through this zoom link. Tuesday office hours moved to Wednesday, same time. Office hours sign up still use the form in the course Overview page. | Early stopping (new version of optimizers). A3. Introduction to classification. | 07a Optimizers2 |
October
Week | Topic | Lecture Notes | Reading | Assignments |
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Week 7: Oct 3, 5 | Classification. Convolutional Networks. | A3 NeuralNetwork Class Using Optimizers due Thursday, October 5th, 10:00 PM | ||
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.1695572235.txt.gz · Last modified: 2023/09/24 10:17 by anderson