Program Overview

Program Overview

The program is titled "Artificial Intelligence: Representation and Problem Solving" and is designated as 15-281. It is offered in the Fall 2025 semester.

Key Information

• Lecture: Tuesday and Thursday, 11:00 am - 12:20 pm, in Tepper 1403.
• Recitation: Friday afternoon, with specific times and locations varying by section.
• Instructor: Pat Virtue.
• Teaching Assistants: Ellyse Lai, Max Yagnyatinskiy, Sunny Kim, Lisa Huang, Johnny Tran, Steven Yang, AJ Seo, Roy Park.
• Course Assistant: Marcie Baker.

Grading

Grades will be collected in Canvas and composed of:

• 15% Midterm exams (each).
• 30% Final exam.
• 20% Programming homework.
• 10% Written homework.
• 5% Online homework.
• 5% Participation.

Textbook

The recommended textbook is "Artificial Intelligence: A Modern Approach, Fourth Edition".

Course Description

This course is about the theory and practice of Artificial Intelligence. It covers modern techniques for computers to represent task-relevant information and make intelligent decisions towards the achievement of goals. Topics include search and problem-solving methods, knowledge representation, decision-making under uncertainty, and learning from experience. The course also explores applications related to AI for Social Good and discusses AI and Ethics.

Prerequisites and Corequisites

• Prerequisites:
  • 15-122: Principles of Imperative Computation.
  • 21-241: Matrices and Linear Transformations.
  • 21-127: Concepts of Mathematics or 15-151: Mathematical Foundations of Computer Science.
• Corequisite:
  • 21-122: Integration and Approximation.

Office Hours

Instructors and teaching assistants have scheduled office hours. Additional "OH" (or "Open") appointment slots are available on the instructor's office hours appointment calendar.

Schedule

The course schedule is subject to change and includes topics such as:

• Introduction to AI.
• Agents and Search.
• Informed Search.
• Adversarial Search.
• Constraint Satisfaction Problems.
• Optimization and Linear Programming.
• Logical Agents.
• Planning.
• Markov Decision Processes.
• Reinforcement Learning.
• Probability.
• Bayes Nets.
• Game Theory.
• Ethics and Human Compatible AI.

Recitations

Recitations start the first week of class and attendance is recommended. Recitation materials are required content and are in-scope for midterm and final exams.

Assignments

There will be five programming assignments and twelve written/online assignments. Assignments include:

• Programming assignments in Python to implement various algorithms.
• Written/online assignments involving working through algorithms, deriving and proving mathematical results, and critically analyzing material presented in class.

Course Notes

Course notes developed by the course staff are available for topics such as:

• Search.
• CSPs.
• Linear and Integer Programming.
• Propositional Logic and Logical Agents.
• Classical Planning.
• Markov Decision Process.
• Reinforcement Learning.
• Probability.
• Bayes Nets.
• HMMs and Particle Filters.
• Game Theory.

Exams

The course includes two midterm exams and a final exam. The midterms will take place in lecture, and the final exam date is scheduled for Friday, December 12, with a makeup on Monday, December 15.

Policies

Grading

Grades are not curved, but final scores are converted to letter grades based on grade boundaries determined at the end of the semester.

Late Policy

• 6 slip days are available across all assignment types.
• Use up to two slip days per assignment.
• Slip days are counted in full 24-hour increments from the assignment deadline.

Collaboration Policy

• Discussions about course content and assignments are encouraged at a conceptual level.
• Viewing, sharing, or communicating about any artifact to be submitted as part of an assignment is not allowed.
• All work presented must be original; using external sources of code or algorithms requires instructor approval.

Accommodations for Students with Disabilities

If you have a disability, discuss your accommodations and needs with the instructor as early in the semester as possible.

Statement of Support for Students' Health & Well-being

The university encourages students to maintain a healthy lifestyle and seek support when needed. Resources are available on campus, including Counseling and Psychological Services (CaPS).