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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. | 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. Examples of good A2 solutions can be found here | |
Week 6: Sept 26, 28 | Early stopping (new version of optimizers). A3. Introduction to classification. | 07a Optimizers2 09 Introduction to Classification Tuesday lecture pre-recorded and available now on Echo360. |
October
Week | Topic | Lecture Notes | Reading | Assignments |
---|---|---|---|---|
Week 7: Oct 3, 5 | Classification with QDA, LDA, and linear logistic regression. | 10 Classification with Linear Logistic Regression | A3 NeuralNetwork Class Using Optimizers due Thursday, October 5th, 10:00 PM | |
Week 8: Oct 10, 12 | Classification with Nonlinear Logistic Regression. Introduction to Reinforcement Learning. | 11 Classification with Nonlinear Logistic Regression Using Neural Networks 12 Introduction to Reinforcement Learning | ||
Week 9: Oct 17, 19 | Reinforcement learning. Learning to play games. | A4 Neural Network Classifier due Wednesday, October 18th, 10:00 PM Modified Oct. 10th, 4:15 PM | ||
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.1697049826.txt.gz · Last modified: 2023/10/11 12:43 by 127.0.0.1