CS47100: Introduction to Artificial Intelligence (Fall 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. 4:30PM-5:30PM
Location: Zoom (See Ed.)

Jiaxin Du

Teaching Assistant

Email: du286 [at] purdue.edu
Office Hour: Thur. 4PM-5PM
Location: HAAS 143

Mehmet Oguz Sakaoglu

Teaching Assistant

Email: msakaogl [at] purdue.edu
Office Hour: Friday 10AM-11AM
Location: HAAS G072

Chiao An Yang

Teaching Assistant

Email: yang2300 [at] purdue.edu
Office Hour: Tuesday 3PM-4PM
Location: HAAS G072

Hairong Yin

Teaching Assistant

Email: yin178 [at] purdue.edu
Office Hour: Thursday 2PM-3PM
Location: HAAS 143

Haomeng Zhang

Teaching Assistant

Email: zhan5050 [at] purdue.edu
Office Hour: Friday 1:30PM-2:30PM
Location: HAAS 143

Nathan Reed

Undergraduate TA

Email: nnreed [at] purdue.edu
Office Hour: Friday 6PM-7PM
Location: HAAS 143

Ananya Singh

Undergraduate TA

Email: singh745 [at] purdue.edu
Office Hour: TBD
Location: TBD

Time & Location

  • Time: Tuesday & Thursday (6:00 pm - 7:15 pm)
  • Location: Wilmeth Active Learning Center (WALC) 1018

Other Resource

Course Schedule

The following schedule is tentative and subject to change.

Aug 22 Lecture 1 Introduction & Overview

AIMA Ch. 1
Aug 24 Lecture 2 AI Representation

AIMA Ch. 2
Aug 28 Info. Assignment 1 released

Select from the following:
Aug 29 Lecture 3 Search - I: Problem Formulation

AIMA Ch. 3.1-3.3
Aug 31 Lecture 4 Search - II: Uninformed Search

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

AIMA Ch. 3.5-3.6
Sep 7 Lecture 6 Local search

AIMA Ch. 4.1
Sep 12 Lecture 7 Adversarial search - I: Minimax

AIMA Ch. 5.1-5.2
Sep 14 Lecture 8 Adversarial search - II: Alpha-Beta Pruning

AIMA Ch. 5.3
Sep 15 Deadline Assignment 1 due (Friday Sep 15, 11:59PM)

Select from the following:
Sep 18 Info. Assignment 2 released

Select from the following:
Sep 19 Lecture 9 CSP - I: Problem Formulation and Inference

AIMA Ch. 6.1-6.2
Sep 21 Lecture 10 CSP - II: Backtracking and Local Search

AIMA Ch. 6.3-6.5
Sep 26 Lecture 11 Logic - I: Propositional Logic

AIMA Ch. 7.2-7.4
Sep 28 Lecture 12 Logic - II: Propositional Theorem Proving

AIMA Ch. 7.5-7.6
Oct 3 Lecture 13 Logic - III: First Order Logic Senmatics

AIMA Ch. 8.2-8.3
Oct 5 Lecture 14 Logic - IV: First Order Logic Inference

AIMA Ch. 9.1-9.5
Oct 10 Info. Fall Break

Select from the following:
Oct 12 Lecture 15 Probability and Uncertainty

AIMA Ch. 12.2-12.6
Oct 13 Deadline Assignment 2 due (Friday Oct 13, 11:59PM)

Select from the following:
Oct 17 Lecture 16 Midterm Review

Oct 19 --- No class (Evening midterm exam)

Select from the following:
Oct 19 Exam Evening midterm exam (8:00PM - 10:00PM)

Select from the following:
Oct 23 Info. Assignment 3 released

Select from the following:
Oct 24 Lecture 17 Bayesian Networks - I: Representation and Semantics

AIMA Ch. 13.1-13.2
Oct 26 Lecture 18 Bayesian Networks - II: Independence

Oct 31 Lecture 19 Bayesian Networks - III: Inference

AIMA Ch. 13.3-13.4
Nov 2 Lecture 20 Markov Decision Process - I: Problem Formulation

AIMA Ch. 17.1
Nov 7 Lecture 21 Markov Decision Process - II: Value Iteration

AIMA Ch. 17.2.1
Nov 9 Lecture 22 Markov Decision Process - III: Policy Iteration

AIMA Ch. 17.2.2
Nov 10 Deadline Assignment 3 due (Friday Nov. 10, 11:59PM)

Select from the following:
Nov 13 Info. Assignment 4 released

Select from the following:
Nov 14 Lecture 23 Reinforcement Learning - I: Problem Formulation

AIMA Ch. 22.1-22.2
Nov 16 Lecture 24 Reinforcement Learning - II: Q-Learning

AIMA Ch. 22.3
Nov 21 Lecture 25 Supervised Learning - I: Overview

AIMA Ch. 19.1-19.2
Nov 23 Info. No class (Thanksgiving Break)

Select from the following:
Nov 28 Lecture 26 Supervised Learning - II: Model Search and Evaluation

AIMA Ch. 19.4
Nov 30 Lecture 27 Supervised Learning - III: Deep Learning

AIMA Ch. 21.1
Dec 1 Deadline Assignment 4 due (Friday Dec 1, 11:59PM)

Select from the following:
Dec 5 Lecture 28 Computer Vision

AIMA Ch. 25
Dec 7 Lecture 29 Final Review

Dec 14 Exam Final Exam (7:00PM-9:00PM)

Select from the following:


Late & Absence Policy

A 10% penalty will be applied (per day) to late assignments. Assignments that are more than two days late will not be accepted. For the consistency and fairness to all students, we follow the policy and absence request through the Office of the Dean of Students.

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.