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The following schedule is tentative and is being updated.
All students may attend the lecture remotely using this zoom link.
August
Week | Topic | Lecture Notes | Reading | Assignments |
---|---|---|---|---|
Week 1: Aug 20, 22 | Course overview. Machine Learning and AI: History and Present Boom Jupyter notebooks. | 01 Introduction to CS545 01a Simple Animations 02 Searching for Good Weights in a Linear Model | JupyterLab Introduction, watch the video then play with jupyter lab. 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 27, 29 | Optimization algorithms. Simple linear and nonlinear models. Confidence intervals. | 02 Searching for Good Weights in a Linear Model 02a Input Importance and Generative AI---Friend or Foe 03 Fitting Simple Models Using Gradient Descent in the Squared Error 04 Training Multiple Models to Obtain Confidence Intervals |
September
Week | Topic | Lecture Notes | Reading | Assignments |
---|---|---|---|---|
Week 3: Sept 3, 5 | Introduction to neural networks. Design of NeuralNetwork class. Optimizers. | 05 Introduction to Neural Networks 06 Python Classes 07 Optimizers Simple | A1 due Friday, September 6th, 10:00 PM. Updated August 28, noon. A1grader.zip is now available. | |
Week 4: Sept 10, 12 | Memory organization for neural network parameters. Optimizers tailored for neural networks. | 08 Collecting All Weights into One-Dimensional Vector for Use in Optimizers 08a Optimizers | Weight Initialization for Deep Learning Neural Networks, by Jason Brownlee | |
Week 5: Sept 17, 19 | Using optimizers. | |||
Week 6: Sept 24, 26 | Early stopping (new version of optimizers). A3. Introduction to classification. |
October
Week | Topic | Lecture Notes | Reading | Assignments |
---|---|---|---|---|
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 9: Oct 15, 17 | Reinforcement learning with Q Function as Neural Network. Learning to play games. | Last Week in AI Geoffrey Hinton: AI Dangers, on 60 Minutes | ||
Week 10: Oct 22, 24 | Modular framework for reinforcement learning. Convolutional Neural Networks. | |||
Week 11: Oct 29, 31 | Pytorch. Jax. Ray. | President Biden's Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence |
November
Week | Topic | Lecture Notes | Reading | Assignments |
---|---|---|---|---|
Week 12: Nov 5, 7 | Convolutional Neural Networks. | |||
Week 13: Nov 12, 14 | Ensembles. Mixture of Experts. | |||
Week 14: Nov 19, 21 | Clustering. K-Nearest Neighbors. Web Apps with Streamlit. | ChatGPT generates fake data set to support scientific hypothesis | ||
Fall Break: Nov 25-29 | No classes. |
December
Week | Topic | Lecture Notes | Reading | Assignments |
---|---|---|---|---|
Week 15: Dec 3, 5 | Word embeddings. Transformers. | |||
Dec 10-12 | Final Exam Week | No Exams in this course |
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