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The following schedule is tentative and is being updated.


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
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.


Week Topic Lecture Notes Reading Assignments
Week 11:
Oct 31 Nov 2
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. This notebook and were updated slightly Oct. 26th, 11:00 AM.
Week 12:
Nov 7, 9
Convolutional Neural Networks. Ensembles. 21 Convolutional Neural Network Class in Pytorch
22 Ensembles
Week 13:
Nov 14, 16
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.
Fall Break:
Nov 20-24
No classes.
Week 14:
Nov 28, 30
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.


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. 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 11-15 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