CS545: Machine Learning

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The following schedule is tentative and is being updated.

All students may attend the lecture remotely using this zoom link.

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

September

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

October

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. Examples of good A3 solutions from your classmates can be found here
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
16 Targets and Deltas Summary 		A4 Neural Network Classifier due Friday, October 18th, 10:00 PM. Examples of good A4 solutions from your classmates can be found here
Week 10:
Oct 22, 24 		Modular framework for reinforcement learning. Parallel processing with ray. Introductions to Pytorch and Jax. 17 Modular Framework for Reinforcement Learning
18 Ray for Parallel Processing
19 More Tic-Tac-Toe and a Simple Robot Arm
19 Introduction to Jax
21 Introduction to Pytorch 		President Biden's Executive Order on Safe, Secure, and Trustworthy Artificial Intelligence Project proposal due at 10 pm Friday evening, October 25th.
Week 11:
Oct 29, 31 		A5.
Pytorch.
Convolutions. 		22 Introduction to Convolutional Neural Networks
23 Convolutional Neural Network in Numpy 		Backpropagation for Convolution by Mayank Kaushik
Convolutions and Backpropagations by Pavithra Solai

November

Week Topic Lecture Notes Reading Assignments
Week 12:
Nov 5, 7 		Convolutional Neural Networks in Pytorch 24 Convolutional Neural Network Class in Pytorch
25 CNN on One-Dimensional Data 		A5 Pole Balancing with Reinforcement Learning Due Friday, November 8th, 10:00 PM. Examples of good A5 solutions can be found here
Week 13:
Nov 12, 14 		Ensembles. Autoencoders. Recurrent Neural Networks. 26 Ensembles
27 Autoencoders
28 Recurrent Networks in Numpy
29 Recurrent Networks in Pytorch
Week 14:
Nov 19, 21 		Word Embeddings. Transformers. Support Vector Machines. 30 Word Embeddings
31 Introdcution to Transformers
32 Support Vector Machines 		ChatGPT generates fake data set to support scientific hypothesis
ClinicalBench: Can LLMs Beat Traditional ML Models in Clinical Prediction?
F1 Score vs. Accuracy: Which Should You Use? 		A6 Convolutional Neural Networks Due Saturday, November 23rd, 10:00 PM.
Fall Break:
Nov 25-29 		No classes.

December

Week Topic Lecture Notes Reading Assignments
Week 15:
Dec 3, 5 		Transformers. Streamlit.
Dec 10-12 Final Exam Week No Exams in this course
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