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

Prerequisites

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.

Textbook

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