Ursinus CS 477: Artificial Intelligence And Machine Learning, Fall 2021
Week 1: AI Jamboards
Here's what you all wrote in class
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General
Overview
Technology Logistics
Readings
Deliverables
Schedule
Grading
Classroom Environment
Collaboration Policy
Other Resources / Policies
Software
Schedule
Assignments
HW0: Python Self Study Module
HW1: Welcome To CS 477
HW2: The Rush Hour Problem
Competition Results
HW3: Markov Chains for Text Processing
HW4: Bayesian Robot Localization
HW5a: 3D Shape Clustering
HW5b: NMF for Music Component Separation
HW6: Logistic Regression on Movie Reviews
HW7: (Deep) Neural Networks on Images
Class Exercises / Notes
Week 1: What Is AI?
Week 1: Choose Your Own Adventure
Student Adventures
Week 1: Monte Carlo COVID Simulation
Solution
Week 2: Blind Maze Searching
Week 2: 8 Puzzle
Week 3: Uniform Cost, Greedy Best-First, and A* Search
Week 4: Markov Chains of Characters
Week 5: Probability Module
Week 5: Bag of Words Exercise / Theory of Bayesian Classifiers
Text Classification Exercise
Naive Bayes Theory
Week 5: Bayes Module
Week 6: Gaussian Naive Bayes And Grad School Admissions
Week 6: Hidden Markov Models / Bayes Filtering / Viterbi Notes
Week 7: Euclidean Vectors / Data Vectorization Module
Week 7: K-Nearest Neighbors And Digits Classification
Week 8: Matrix Module
Week 8: Nonnegative Matrix Factorization
Week 9/10: KMeans Clustering, Applications To Image Processing
Week 10: Visual Bag of Words
Week 10/11: Logistic Regression And Gradient Descent
Week 14: Cat Image Autoencoder
Week 14: Cat or Dog Deep Convolutional Network
Week 15: Voting on Ethical Problems in AI
Ethics Reading / Discussions
Final Ethics Project