schedule_23_fall

Table of Contents

August
September
October
November
December

The following schedule is tentative and is being updated.

August

Week	Topic	Lecture Notes	Reading	Assignments
Week 1: Aug 20, 22	Course overview. Jupyter notebooks. Animations.	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 27, 29	Optimization algorithms. Simple linear and nonlinear models.	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

September

Week	Topic	Lecture Notes	Reading	Assignments
Week 3: Sept 3, 5 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 10, 12	Design of NeuralNetwork class. Optimizers.	06 Python Classes 07 Optimizers	Weight Initialization for Deep Learning Neural Networks, by Jason Brownlee
Week 5: Sept 17, 19	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 24, 26	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 1, 3	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 8, 10	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 15, 17	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 22, 24	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.
Week 11: Oct 29, 31	Ray. Pytorch. Convolutional Neural Networks.	18 Ray for Parallel Processing 19 Introduction to Pytorch 20 Steps Towards Convolutional Nets in Pytorch	President Biden's Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence	A5 Reinforcement Learning due Friday, Nov 3rd, 10:00 PM.

November

Week	Topic	Lecture Notes	Reading	Assignments
Week 12: Nov 5, 7	Convolutional Neural Networks. Ensembles.	21 Convolutional Neural Network Class in Pytorch 22 Ensembles
Week 13: Nov 12, 14	Clustering. K-Nearest Neighbors. Jax.	23 K-Means Clustering, K-Nearest-Neighbor Classification 24 Introduction to Jax	Learning skillful medium-range global weather forecasting by DeepMind, using graph-convolutional_networks (GNNs) implemented in with jax.
Week 14: Nov 19, 21	Support Vector Machines. Web Apps with Streamlit. Word Embeddings.	25 Support Vector Machines 26 Web Apps with Streamlit 27 Word Embeddings	ChatGPT generates fake data set to support scientific hypothesis	A6 Convolutional Neural Networks due Friday, Dec. 1, 10:00 PM.
Fall Break: Nov 25-29	No classes.

December

Week	Topic	Lecture Notes	Reading	Assignments
Week 15: Dec 3, 5	Transformers.	28 Introduction to Transformers 29 Transformers Predicting Text Chuck's Recent Projects: 1. Pretraining Speeds Up RL 2. Brain-Computer 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 10-12	Final Exam Week	No Exams in this course		Project Report due at 10 pm Tuesday evening, December 12th.