Artificial Intelligence (CS 4365)

Course Information

Term: Spring 2024

Course Number: CS 4365

Level: Senior Level

Instructor Role: Guest Lecturer

Lecture Times: TuTh 2:30 PM - 3:45 PM

Location: ECSW 1.315

Office Hours: By appointment

Announcements

  • [2024-01-15] Welcome to CS 4365! Please complete the pre-course survey by Friday.
  • [2024-01-15] First homework will be released this week.
  • [2024-01-15] Make sure to join our Piazza discussion forum.

Course Description

This course introduces the theoretical and computational techniques that serve as a foundation for the study of artificial intelligence (AI). Students will learn fundamental concepts in AI including search algorithms, knowledge representation, reasoning systems, and game theory, while gaining practical experience through programming assignments and projects.

Learning Outcomes

Upon completion of this course, students will be able to:

  1. Implement and analyze uninformed and heuristic search techniques
  2. Apply local search algorithms to optimization problems
  3. Solve constraint satisfaction problems using various techniques
  4. Perform logical inference using the resolution algorithm
  5. Implement probabilistic inference in Bayesian networks
  6. Develop game-playing agents using adversarial search
  7. Analyze zero-sum games with hidden information
  8. Design solutions for non-zero-sum games with hidden information

Prerequisites

Required courses:

  • CS 3345 (Data Structures and Introduction to Algorithmic Analysis)
  • Strong Python programming skills
  • Basic knowledge of probability and statistics
  • Familiarity with mathematical logic

Course Materials

Required Textbook

  • Stuart Russell and Peter Norvig. “Artificial Intelligence: A Modern Approach” (3rd Edition), Prentice Hall

Additional Materials

  • Selected papers and handouts (provided on eLearning)
  • Programming assignments and starter code
  • Lecture slides and notes

Grading

  • Homework Assignments & Projects: 30%
  • Midterm 1: 30%
  • Midterm 2: 40%
  • Class Participation and Attendance: Extra 5%

Important Policies

  • No Make-up Exams: There will be no make-up exams except for documented medical emergencies
  • Late Submissions: 10% penalty per day
  • Extra Credit: Select exam and homework questions will be marked for extra credit
  • Class Attendance: Mandatory
  • Discussion Participation: Extra credit available for active participation

Course Schedule

Week Topics Reading Notes
1-2 Introduction to AI & Intelligent Agents Ch. 1-2 Course overview, AI foundations
3-4 Problem Solving by Search Ch. 3 Uninformed search strategies
5-6 Advanced Search Techniques Ch. 4 Informed search, local search
7 Adversarial Search Ch. 5 Game playing strategies
8 Constraint Satisfaction Ch. 6 CSP techniques
9 Propositional Logic Ch. 7-8 Midterm 1
10 First-order Logic Ch. 9 Knowledge representation
11 Probability Theory Ch. 13 Uncertainty handling
12 Probabilistic Reasoning Ch. 14 Bayesian networks
13 Temporal Probability Models Ch. 15 Dynamic systems
14 Machine Learning Basics Ch. 18 ML foundations
15 Advanced Topics & Review TBD Midterm 2

Assignments & Projects

Programming Assignments

  • Multiple programming assignments throughout the semester
  • Focus on implementing AI algorithms
  • Projects will be done individually unless specified otherwise
  • All code must be original and properly documented

Written Assignments

  • Regular problem sets covering theoretical concepts
  • Analysis of AI algorithms and techniques
  • Mathematical proofs and derivations

Additional Resources

Course Resources

  • Course Website: eLearning
  • Discussion Forum: Piazza
  • Programming Environment: Python 3.x
  • Version Control: Git

Additional Resources

  • Office Hours Schedule
  • Python Tutorial Resources
  • AI Research Paper Repository
  • Practice Problems and Solutions

Course Policies

Academic Integrity

Academic integrity is fundamental to the academic enterprise. Students are expected to work independently on all assignments and exams unless explicitly instructed otherwise. Any form of cheating, plagiarism, or unauthorized collaboration will result in:

  • Failing grade for the assignment/exam
  • Possible course failure
  • Referral to the Dean of Students

Regrade Policy

  • Regrade requests must be submitted within two weeks of grade posting
  • Requests must include specific explanation of the issue
  • Entire assignment may be regraded
  • Final grade decisions are made at the instructor’s discretion

Communication Policy

  • Use Piazza for all course-related questions
  • Email only for personal matters
  • Responses typically within 24 hours on weekdays

Additional Information