CS47100: Introduction to Artificial Intelligence (Fall 2026)
A class on Artificial Intelligence at Purdue University, 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.
The course materials, e.g., slides, schedule, projects, are adapted from Berkeley's CS188 course.
Pre-requisites:
- CS251 Data Structures (grade of C or better)
Textbook:
- [AIMA] S. Russell and P. Norvig (2020). Artificial Intelligence: A Modern Approach. Pearson, 4th Edition. (ISBN:9780134610993)
- You can also use the 3rd edition and find the corresponding sections to read.
Grading:
- Assignments: 25% (6.25% for each assignment)
- Midterm: 37.5%
- Final Exam: 37.5%
FAQ:
- Lecture slides and recordings will be posted on Brightspace.
- The instructors & TAs can be best reached through Ed Discussion. Please post your questions there instead of emailing TAs.
- During office hours or on Ed Discussion, please avoid posting partial homework solutions or asking TAs to "review" your code/solution.
- Tutorial for learning Latex with Overleaf: [Link]
Instructors
Raymond A. Yeh
Instructor
Email: rayyeh
Office Hour: TBD
Location: TBD
Brian Bullins
Instructor
Email: bbullins
Office Hour: TBD
Location: TBD
Teaching Assistants
Vikhyat Agarwal
Teaching Assistant
Email: agarw682
Office Hour: TBD
Location: TBD
Jiaxin Du
Teaching Assistant
Email: du286
Office Hour: TBD
Location: TBD
Jimson J. Huang
Teaching Assistant
Email: huan2073
Office Hour: TBD
Location: TBD
Nathan N. Reed
Teaching Assistant
Email: nnreed
Office Hour: TBD
Location: TBD
Abhijeet Vyas
Teaching Assistant
Email: vyas26
Office Hour: TBD
Location: TBD
Jinzhi Yang
Teaching Assistant
Email: yang3056
Office Hour: TBD
Location: TBD
Hairong Yin
Teaching Assistant
Email: yin178
Office Hour: TBD
Location: TBD
Jincheng Zhou
Teaching Assistant
Email: zhou791
Office Hour: TBD
Location: TBD
Kevin Zhang
Teaching Assistant
Email: zhan4196
Office Hour: TBD
Location: TBD
Time & Location
- Time (LE1): Tuesday & Thursday (3:00-4:15 PM)
- Location (LE1): BRNG 1278
- Time (LE2): Tuesday & Thursday (1:30-2:45 PM)
- Location (LE2): MATH 175
Other Resource
Course Schedule
The following schedule is tentative and subject to change.
| Date | Event | Description | Readings |
|---|---|---|---|
| August 25 | Lecture 1 | Introduction to AI
|
AIMA Ch. 1 |
| August 27 | Lecture 2 | AI Representation
|
AIMA Ch. 2 |
| August 31 | Info. | Assignment 1 released
Select from the following: |
|
| September 1 | Lecture 3 | Search - I: Problem Formulation
|
AIMA Ch. 3.1-3.3 |
| September 3 | Lecture 4 | Search - II: Uninformed Search
|
AIMA Ch. 3.4 |
| September 8 | Lecture 5 | Search - III: Informed search
|
AIMA Ch. 3.5-3.6 |
| September 10 | Lecture 6 | Local Search
|
AIMA Ch. 4.1 |
| September 15 | Lecture 7 | Adversarial search - I: Minimax
|
AIMA Ch. 5.1-5.2 |
| September 17 | Lecture 8 | Adversarial search - II: Alpha-Beta Pruning
|
AIMA Ch. 5.3 |
| September 18 | Deadline | Assignment 1 due (Friday September 18, 11:59PM)
Select from the following: |
|
| September 21 | Info. | Assignment 2 released
Select from the following: |
|
| September 22 | Lecture 9 | CSP - I: Problem Formulation and Inference
|
AIMA Ch. 6.1-6.2 |
| September 24 | Lecture 10 | CSP - II: Backtrack Search
|
AIMA Ch. 6.3-6.5 |
| September 29 | Lecture 11 | Logic - I: Propositional Logic
|
AIMA Ch. 7.2-7.4 |
| October 1 | Lecture 12 | Logic - II: Propositional Theorem Proving
|
AIMA Ch. 7.5-7.6 |
| October 6 | Lecture 13 | Probability and Uncertainty
|
AIMA Ch. 12.2-12.6 |
| October 8 | Lecture 14 | Midterm Review (Last Lecture of Prof. Yeh)
|
|
| October 9 | Deadline | Assignment 2 due (Friday October 9, 11:59PM)
Select from the following: |
|
| October 13 | Info. | No class (Fall break)
Select from the following: |
|
| October 15 | Exam | Evening midterm exam (8:00PM - 10:00PM)
Select from the following: |
|
| October 15 | Info. | No class (Evening midterm exam)
Select from the following: |
|
| October 19 | Info. | Assignment 3 released
Select from the following: |
|
| October 20 | Lecture 15 | Bayesian Networks - I: Representation and Semantics
|
AIMA Ch. 13.1-13.2 |
| October 22 | Lecture 16 | Bayesian Networks - II: Independence
|
|
| October 27 | Lecture 17 | Bayesian Networks - III: Inference
|
AIMA Ch. 13.3-13.4 |
| October 29 | Lecture 18 | Markov Decision Process - I: Problem Formulation
|
AIMA Ch. 17.1 |
| November 3 | Lecture 19 | Markov Decision Process - II: Value Iteration
|
AIMA Ch. 17.2.1 |
| November 5 | Lecture 20 | Markov Decision Process - III: Policy Iteration
|
AIMA Ch. 17.2.2 |
| November 6 | Deadline | Assignment 3 due (Friday November 6, 11:59PM)
Select from the following: |
|
| November 9 | Info. | Assignment 4 released
Select from the following: |
|
| November 10 | Lecture 21 | Reinforcement Learning - I: Problem Formulation
|
AIMA Ch. 22.1-22.2 |
| November 12 | Lecture 22 | Reinforcement Learning - II: Q-Learning
|
AIMA Ch. 22.3 |
| November 17 | Lecture 23 | Supervised Learning - I: Overview
|
AIMA Ch. 19.1-19.2 |
| November 19 | Lecture 24 | Supervised Learning - II: Model Search and Evaluation
|
AIMA Ch. 19.4 |
| November 24 | Lecture 25 | Supervised Learning - III: Linear Models
|
AIMA Ch. 19.6 |
| November 26 | Info. | No class (Thanksgiving)
Select from the following: |
|
| December 1 | Lecture 26 | Supervised Learning - IV: Optimization
|
|
| December 3 | Lecture 27 | Supervised Learning - V: Deep learning
|
AIMA Ch. 21.1 |
| December 4 | Deadline | Assignment 4 due (Friday December 4, 11:59PM)
Select from the following: |
|
| December 8 | Lecture 28 | Extra Topic (TBD)
|
|
| December 10 | Lecture 29 | Final Review
|
|
| Dec. 14-19 | Exam | Final Exam (date & time TBD)
Select from the following: |