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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 AIFriend 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 OneDimensional 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 prerecorded 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 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 Examples of good A4 solutions can be found here And here are python script files as A4 answer. 
Week 10: Oct 24, 26  Modular framework for reinforcement learning. Convolutional Neural Networks.  16 Modular Framework for Reinforcement Learning 17 Convolutional Neural Networks  Project proposal due at 10 pm Friday evening, October 27th. 
November
December
Week  Topic  Lecture Notes  Reading  Assignments 

Week 15: Dec 5, 7  Transformers.  28 Introduction to Transformers 29 Transformers Predicting Text Chuck's Recent Projects: 1. Pretraining Speeds Up RL 2. BrainComputer Interfaces 3. Explaining a Neural Network's Decisions  Automatic detection of hallucination with SelfCheckGPT Embers of Autoregression: Understanding Large Language Models Through the Problem They are Trained to Solve  
Dec 1115  Final Exam Week  No Exams in this course  Project Report due at 10 pm Tuesday evening, December 12th. 
start.txt · Last modified: 2023/12/07 09:17 by 127.0.0.1