CSCI 4150 Introduction to Artificial Intelligence (Spring 2026)

General Information

Course Number: CSCI 4150
Lecture Hours: 12:00 noon - 1:50 pm, Monday and Thursday
Location: DARRIN 308

Instructor

Yao Ma
Email: may13@rpi.edu
Office: MRC 330C

Course Overview

This course provides an introduction to the principles and practices of Artificial Intelligence (AI). Students will learn foundational AI techniques and concepts, including search algorithms, knowledge representation, machine learning, reasoning, and applications in various fields.

Book

Artificial Intelligence: A Modern Approach, 4th Edition by Stuart Russell and Peter Norvig

Most content can also be found at Introduction to Artificial Intelligence

Homework Assignments

There will be 3-4 homework assignments.

Project

There will be about 4 projects. You are expected to program in python for these projects.

Exams

There will be two in-class exams.

Grading

  • Homework Assignments: 30%

  • Project: 30%

  • Midterm Exam: 20%

  • Final Exam: 20%

Late Policy

  • 1 day: -20%

  • 2 days: -50%

  • 3 days: -100%

Re-grading policy

  • Report grading issues within one week of grades being available.

  • For the exams, requests must be made within 2 days.

Academic Integrity

Students must work independently on all course assignments. You may consult other members of the class on the assignments, but you must submit your own work. For instance you may discuss general approaches to solving a problem, but you must implement the solution on your own (similarity detection software may be used). Anytime you borrow material from the web or elsewhere, you must acknowledge the source. Copying and pasting from published sources or the internet is considered plagiarism and is not acceptable. Plagiarized work will receive an automatic grade of zero.

Student-teacher relationships are built on trust. Acts which violate this trust undermine the educational process. The Rensselaer Handbook of Student Rights and Responsibilities and The Rensselaer Graduate Student Supplement define various forms of Academic Dishonesty and procedures for responding to them. Submission of any assignment that is in violation with these policies will result in a penalty that is deemed by the instructor to be appropriate to the infraction ranging from a grade of zero on the assignment in question, to failure of the class as a whole. The student will also be reported to the Dean of Students or the Dean of Graduate Education as appropriate. Note that academic dishonesty will be dealt with severely and will be reported to the Dean of Students. If you have any questions concerning this policy before submitting an assignment, please ask for clarification.

Course Content and Tentative Schedule

Date Topic Note
01/12 Course Introduction; History and Foundations of AI
01/15 Search I
01/19 MLK Day - no classes
01/22 Search II
01/26 Constraint Satisfaction Problems
01/29 Logic
02/02 Game Trees I
02/05 Game Trees II
02/09 Markov Decision Processes Intro
02/12 Markov Decision Processes I
02/16 Presidents’ Day - no classes
02/17 Markov Decision Processes II Follow a Monday class schedule today
02/19 Reinforcement Learning I
02/23 Recap for Exam 1
02/26 In-class Exam 1
03/02 Spring Break - no classes
03/05 Spring Break - no classes
03/09 Introduction to Probability
03/12 Bayes Nets I
03/16 Bayes Nets II
03/19 Bayes Nets III
03/23 Decision Networks and Value of Information
03/26 Hidden Markov Models
03/30 Machine Learning I
04/02 Machine Learning II
04/06 Machine Learning III
04/09 AI Applications
04/13 AI Ethics, Safety, and Security
04/16 Wrap-up Guest lecture Buffer
04/20 Recap for Exam 2
04/23 In-class Exam 2