CS545: Machine Learning

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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

Week	Topic	Lecture Notes	Reading	Assignments
Week 3: Sept 3, 5	Introduction to neural networks.	05 Introduction to Neural Networks	3Blue1Brown Introduction to Neural Networks in the first five chapters provides a fun video tutorial including error backpropagation.
Week 4: Sept 10, 12	Design of NeuralNetwork class. Optimizers. Overview of A2. Memory organization for neural network parameters. Optimizers tailored for neural networks.	06 Python Classes 07 Optimizers Simple 08 Collecting All Weights into One-Dimensional Vector for Use in Optimizers 08a Optimizers	Weight Initialization for Deep Learning Neural Networks, by Jason Brownlee	A1 due Monday, September 9th, 10:00 PM.
Week 5: Sept 17, 19 Chuck's office hours cancelled today.	Introduction to Classification.	09 Introduction to Classification		A2 NeuralNetwork Class due Thursday, September 19, 10:00 PM. Here is an example solution to A2: A2 NeuralNetwork Class Solution. Examples of good A2 solutions from your classmates can be found here
Week 6: Sept 24, 26	Classification with Logistic Regression.	10 Classification with Linear Logistic Regression 11 Classification with Nonlinear Logistic Regression Using Neural Networks

Week	Topic	Lecture Notes	Reading	Assignments
Week 7: Oct 1, 3	Classification with Nonlinear Logistic Regression, K-Nearest-Neighbors. Clustering with K-Means.	12 K-Means Clustering, K-Nearest-Neighbor Classification		A3 NeuralNetwork Class Using Optimizers due Friday, October 4th, 10:00 PM. A3grader.zip is now available.
Week 8: Oct 8, 10	A4. Introduction to Reinforcement Learning.	13 Introduction to Reinforcement Learning 14 Reinforcement Learning with Neural Networks as Q Function	John Hopfield and Geoffrey Hinton awarded Nobel Physics Prize Will digital intelligence replace biological intelligence? Geoffrey Hinton Warns of the “Existential Threat” of AI
Week 9: Oct 15, 17	Reinforcement learning with Q Function as Neural Network. Learning to play games.	14 Reinforcement Learning with Neural Networks as Q Function 15 Reinforcement Learning for Two Player Games		A4 Neural Network Classifier due Thursday, October 17th, 10:00 PM. A4grader.zip was updated Oct. 11th, 9:30 am.
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

Week	Topic	Reading
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.

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