CS545: Machine Learning

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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

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.

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 Examples of good A3 solutions can be found here
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 with Q Function as Neural Network. Learning to play games.	13 Reinforcement Learning with Neural Networks as Q Function 14 Targets and Deltas Summary 15 Reinforcement Learning for Two Player Games	Last Week in AI Geoffrey Hinton: AI Dangers, on 60 Minutes	A4 Neural Network Classifier due Wednesday, October 18th, 10:00 PM
Week 10: Oct 24, 26	Modular framework for reinforcement learning. Convolutional Neural Networks. Pytorch.	16 Modular Framework for Reinforcement Learning 17 Convolutional Neural Networks		Project proposal due at 10 pm Friday evening, October 27th.

Week	Topic	Lecture Notes	Assignments
Week 11: Oct 31 Nov 2	Pytorch and Convolutional Neural Networks	18 Introduction to Pytorch 19 Convolutional Neural Networks in Pytorch	A5 Reinforcement Learning due Friday, Nov 3rd, 10:00 PM. This notebook and a5.zip were updated slightly Oct. 26th, 11:00 AM.
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.

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