CS47100: Introduction to Artificial Intelligence (Spring 2023)

Images generated from DALL-E-2 with text prompt A class on Artificial Intelligence, digital art.

Course Information

Artificial intelligence (AI) is about building intelligent machines that can perceive and act rationally to achieve their goals. To prepare students for this endeavor, we cover the following topics in this course: Search, constraint satisfaction, logic, reasoning under uncertainty, machine learning, and planning. There will be four assignments in the form of both written and programming problems.





Instructor & TAs

Raymond A. Yeh


Email: rayyeh [at] purdue.edu
Office Hour: Mon 4-5PM
Location: Zoom

Jiaxin Du

Teaching Assistant

Email: du286 [at] purdue.edu
Office Hour: Thu 4-5PM
Location: Zoom

Jinzhao Li

Teaching Assistant

Email: li4255 [at] purdue.edu
Office Hour: Fri 10-11AM
Location: Zoom

Zhuoyan Li

Teaching Assistant

Email: li4178 [at] purdue.edu
Office Hour: Wed 10-11AM
Location: Zoom

Mir Imtiaz Mostafiz

Teaching Assistant

Email: mmostafi [at] purdue.edu
Office Hour: Fri 12PM-1PM
Location: Zoom

Xinru Wang

Teaching Assistant

Email: xinruw [at] purdue.edu
Office Hour: Tue 10:30-11:30AM
Location: Zoom

Ananya Singh

Undergraduate TA

Email: singh745 [at] purdue.edu
Office Hour: Mon 3-4PM
Location: Zoom

Time & Location

  • Time: Mon. & Wed. (5:30 pm - 6:45 pm)
  • Location: Lilly Hall of Life Sciences G126

Other Resource

Course Schedule

The following schedule is tentative and subject to change.

Jan. 9 Lecture 1 Introduction & Overview

AIMA Ch. 1
Jan. 11 Lecture 2 AI Representation

AIMA Ch. 2
Jan. 16 --- Martin Luther King Jr. Day (No Classes)

Select from the following:
Jan. 18 Lecture 3 Search - I: Problem Formulation

AIMA Ch. 3.1-3.3
Jan 23 Info. Assignment 1 released

Select from the following:
Jan 23 Lecture 4 Search - II: Uninformed Search

AIMA Ch. 3.4
Jan 25 Lecture 5 Search - III: Informed search

AIMA Ch. 3.5-3.6
Jan 30 Lecture 6 Local search

AIMA Ch. 4.1
Feb 1 Lecture 7 Adversarial search - I: Minimax

AIMA Ch. 5.1-5.2
Feb 6 Lecture 8 Adversarial search - II: Alpha-Beta Pruning

AIMA Ch. 5.3
Feb 8 Lecture 9 CSP - I: Problem Formulation and Inference

AIMA Ch. 6.1-6.2
Feb 13 Lecture 10 CSP - II: Backtracking and Local Search

AIMA Ch. 6.3-6.5
Feb 15 Lecture 11 Logic - I: Propositional Logic

AIMA Ch. 7.2-7.4
Feb 17 Deadline Assignment 1 due (Friday 17, 11:59PM)

Select from the following:
Feb 20 Info. Assignment 2 released

Select from the following:
Feb 20 Lecture 12 Logic - II: Propositional Theorem Proving

AIMA Ch. 7.5-7.6
Feb 22 Lecture 13 Logic - III: First Order Logic Senmatics

AIMA Ch. 8.2-8.3
Feb 27 Lecture 14 Logic - IV: First Order Logic Inference

AIMA Ch. 9.1-9.5
Mar 1 Lecture 15 Probability and Uncertainty

AIMA Ch. 12.2-12.6
Mar 3 Deadline Assignment 2 due (Friday Mar 3, 11:59PM)

Select from the following:
Mar 6 Lecture 16 Midterm Review

Mar 8 --- No class (Evening midterm exam)

Select from the following:
Mar 8 Exam Evening midterm exam (8:00PM - 10:00PM)

Select from the following:
Mar 13 --- Spring Break

Select from the following:
Mar 15 --- Spring Break

Select from the following:
Mar 20 Info. Assignment 3 released

Select from the following:
Mar 20 Lecture 17 Bayesian Networks - I: Representation and Semantics

AIMA Ch. 13.1-13.2
Mar 22 Lecture 18 Bayesian Networks - II: Independence

Mar 27 Lecture 19 Bayesian Networks - III: Inference

AIMA Ch. 13.3-13.4
Mar 29 Lecture 20 Markov Decision Process - I: Problem Formulation

AIMA Ch. 17.1
Apr 3 Lecture 21 Markov Decision Process - II: Value Iteration

AIMA Ch. 17.2.1
Apr 5 Lecture 22 Markov Decision Process - III: Policy Iteration

AIMA Ch. 17.2.2
Apr 7 Deadline Assignment 3 due (Friday Apr 7, 11:59PM)

Select from the following:
Apr 10 Info. Assignment 4 released

Select from the following:
Apr 10 Lecture 23 Reinforcement Learning - I: Problem Formulation

AIMA Ch. 22.1-22.2
Apr 12 Lecture 24 Reinforcement Learning - II: Q-Learning

AIMA Ch. 22.3
Apr 17 Lecture 25 Supervised Learning - I: Overview

AIMA Ch. 19.1-19.2
Apr 19 Lecture 26 Supervised Learning - II: Model Search and Evaluation

AIMA Ch. 19.4
Apr 23 Deadline Assignment 4 due (Sunday, Apr 23 11:59PM)

Select from the following:
Apr 24 Lecture 27 Computer Vision

Apr 26 Lecture 28 Final Review

May 1 Exam Final Exams (7:00PM to 9:00PM)

Select from the following:


Late Policy

A 10% penalty will be applied (per day) to late assignments. Assignments that are more than two days late will not be accepted.

Academic Honesty

Please refer to Purdue's Student Guide for Academic Integrity. Academic dishonesty will result in an automatic zero on an assignment and your course grade will be reduced by one full letter grade. A second attempt will result in a failing grade for the course. It is one's responsibility to prevent others from copying your work.


Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, please contact the Disability Resource Center at: drc@purdue.edu or by phone at 765-494-1247 and the course instructor to arrange for accommodations.

Classroom Guidance Regarding Protect Purdue

Any student who has substantial reason to believe that another person is threatening the safety of others by not complying with Protect Purdue protocols is encouraged to report the behavior to and discuss the next steps with their instructor. Students also have the option of reporting the behavior to the Office of the Student Rights and Responsibilities. See also Purdue University Bill of Student Rights and the Violent Behavior Policy under University Resources in Brightspace.

University Policies

Please refer to additional university policies in BrightSpace.