CS47100: Introduction to Artificial Intelligence (Fall 2023)
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.
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: 40% (10% each)
- Midterm: 30%
- Final Exam: 30%
FAQ:
- Lecture slides and recordings will be posted on Brightspace.
- The instructor & 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]
Instructor & TAs
Raymond A. Yeh
Instructor
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.
| Date | Event | Description | Readings |
|---|---|---|---|
| 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: |