CMPSC 442 Spring 2024: Artificial Intelligence

Course Information

Instructor Lecture Time and Location TA Course Syllabus

Course Goals and Objectives

This course provides an overview of the foundations, problems, approaches, implementation, and applications of artificial intelligence. Topics covered include problem solving, goal-based and adversarial search, logical, probabilistic, and decision theoretic knowledge representation and inference, decision making, and learning. Through programming assignments that sample these topics, students acquire an understanding of what it means to build rational agents of different sorts as well as applications of AI techniques in language processing, planning, vision. The goal of this course is to learn
  1. Basic techniques for building intelligent computer systems
    1. Search, (games,) constraint satisfaction, uncertainty and probability, Bayes Rule, Naïve Bayes, Hidden Markov Models
    2. Introduction to fundamental concepts in machine learning: linear regression, linear regression classifier, perceptron learning rule
  2. In depth consideration of the role AI will play in our lives


Enforced Prerequisite at Enrollment: CMPSC 221. Enforced Concurrent at Enrollment: CMPSC 465. Students are expected to have a good understanding of probability, data structures, and programming. We will cover Python basics early in the semester. Students should feel comfortable programming, debugging, and testing in Python.


Programming Assignments

There are 6 programming assignments. The programming assignments shall be done with Python 3.6+. Every programming assignment will be graded by an autograder. Do not submit with import errors or syntax errors. You must use the template *py files. Your code must produce a correct or partly correct answer to get full or partial credit. All submissions must be your own dependent work. Do not copy other people's code or misrepresent it as yours. We will use high grade plagiarism detection code. Late Policy for Programming Assignments. There is only 1 late day allowed with a penalty of 25% for EACH programming assignment. For example, if you obtain a raw score 90 on the first late day, you will get 90x75%= 67.5; if you submit two days after the deadline, the score is 0.

Course Schedule

Date Topic Material / Reading Event Due
Week 1
Tuesday Jan 9
AIMA Chapter 1.1 1.2
Thursday Jan 11 Intelligent Agents
AIMA Chapter 2.1 2.2 2.3 2.4
Week 2
Tuesday Jan 16
Class Cancelled HW 1 Out
Thursday Jan 18
AIMA Chapter 3
Week 3
Tuesday Jan 23
Uninformed Search
AIMA Chapter 3
Thursday Jan 25 Informed Search
AIMA Chapter 3
Week 4
Tuesday Jan 30
Games and Adversarial Search
AIMA Chapter 5
Thursday Feb 1 Games and Adversarial Search
AIMA Chapter 5 HW 1 Due
Week 5
Tuesday Feb 6
Non-classical Search
AIMA Chapter 4 HW 2 Out
Thursday Feb 8 Non-classical Search
AIMA Chapter 4
Week 6
Tuesday Feb 13
Constraint Satisfaction Problem
AIMA Chapter 6
Thursday Feb 15 Constraint Satisfaction Problem
AIMA Chapter 6
Week 7
Tuesday Feb 20
HW 3
HW 3 Out
Thursday Feb 22 Logical Agents
AIMA Chapter 7 HW 2 Due
Week 8
Tuesday Feb 27
Propositional Logic
AIMA Chapter 7
Thursday Feb 29 First-Order Logic
AIMA Chapter 8 HW 3.1 Due
Week 9
Tuesday March 5
Spring Break
Thursday March 7 Spring Break
Week 10
Tuesday March 12
Logical Inference
AIMA Chapter 7,8,9 HW 4 Out
Thursday March 14 Probabilistic Agents
AIMA Chapter 13
Week 11
Tuesday March 19
Naive Bayes Classifier
AIMA Chapter 13, 20 HW 3.2 Due
Thursday March 21 Naive Bayes Classifier
AIMA Chapter 13, 20
Week 12
Tuesday March 26
Bayesian Networks
AIMA Chapter 14 HW 4 Due
Thursday March 28 Bayesian Networks
AIMA Chapter 14 HW 5 Out
Week 13
Tuesday April 2
Bayesian Networks
AIMA Chapter 14
Thursday April 4 Hidden Markov Models
SLP Appendix A
Week 14
Tuesday April 9
Hidden Markov Models
SLP Appendix A
Thursday April 11 Hidden Markov Models
SLP Appendix A HW 6 Out HW 5 Due
Week 15
Tuesday April 16
Machine Learning
Thursday April 18 Neural Networks and Transformers
SLP Chapter 7, 10
Week 16
Tuesday April 23
Large Language Models
SLP Chapter 10
Thursday April 25 AI Ethics
On the opportunities and risks of foundation models HW 6 Due
Week 17
Thursday May 2
Final Exam Due