CSCE 625: Artificial Intelligence (Fall 2018)

 

 

Instructor

Dr. Zhangyang (Atlas) Wang

Email: atlaswang@tamu.edu

Office: 328C HRBB (visit by appointment only except office hours)

Webpage: http://www.atlaswang.com

 

TA: Ye Yuan

Email: ye.yuan@tamu.edu

Office: 320 HRBB

 

Grader: Siru Li

Email: li1994@tamu.edu

 

Time and Location

·       Lecture time: 9:35 - 10:50 am, every Tuesday and Thursday

·       Lecture location: ZACH 350

·       Instructor Office Hour: 1:00 2:00 pm every Tuesday

·       TA Office Hour:  2:00 3:00 pm every Thursday

·       Class fully seated. NO AUDITION ALLOWED.

 

Course Description

Basic concepts and methods of artificial intelligence. Review of related mathematical background. Introduction to machine learning.

 

Course Goal

The students will digest and practice their knowledge and skills by class discussion and exams, and obtain in-depth experience with a particular topic through a final project.

 

Evaluation Metrics

Grading will be based on two mid-term exams (30% each), and one final project (40%) (proposal 10% + presentation 10% + code review 10% + report 10%).  There will be no final exam. 

 

Final project Collaborations and teamwork are encouraged, but must be coordinated and approved by the instructor. A team can only have 3 members.

The project proposal, report and codes should be all submitted via email. For late submission, each additional late day will incur a 10% penalty.

 

The grading policy is as follows:

90-100:

A

80-89:

B

70-79:

C

60-69:

D

<60:

F

 

Project

It's important that you work on a real AI project, or a real problem in some relevant domain, so that you earn first-hand experience. The instructor is available to discuss and shape the project. The scale of the project should be scheduled as one semester long. This year, we will host a project competition, and the scope details will be announced in class.

 

By the end of the semester, you should submit your code and data for this project, write a project report of maximum 8 pages (plus additional pages containing only references) using the standard CVPR paper template, and prepare a class presentation. The instructor will be happy to help develop promising project ideas into a formal publication during or after the semester, if you wish so.

 

 

Course Project Sponsor

 

Prerequisite

·       Students should have taken the following courses or equivalent: Data Structure and Algorithms (CSCE 221), Linear Algebra (MATH 304 or MATH 323), Numerical Methods (MATH 417).

·       Coding experiences with Matlab, C/C++ or Python are assumed.

·       Previous knowledge of machine learning, computer vision signal processing or data mining will be helpful, but not necessary.

 

Reading Materials

This course does not follow any textbook closely. Among many recommended readings are:

1.     Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, 3rd edition.

2.     Pattern Recognition and Machine Learning, Christopher M. Bishop (2006).

3.     The Elements of Statistical Learning, Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie (2001), Springer.

4.     Convex Optimization, Stephen Boyd and Lieven Vandenberghe (2004), Cambridge University Press. [Their CVX toolbox is a great Matlab-based convex optimization tool for beginners]

5.     Linear Algebra and its Applications, Gilbert Strang (1988). [For a concise reference to keep at hand, my best recommendation is definitely The Matrix Cookbook]

6.     Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016), MIT Press.

 

Lecture Notes (in PDF format) will be uploaded to the course webpage no more than 24 hours AFTER each class.

 

Attendance and Make-up Policies

Every student should attend the class, unless you have an accepted excuse. Please check student rule 7 http://student-rules.tamu.edu/rule07 for details.

 

Academic Integrity

Aggie Code of Honor: An Aggie does not lie, cheat or steal, or tolerate those who do. see: Honor Council Rules and Procedures

 

Americans with Disabilities Act (ADA) Statement

The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you believe you have a disability requiring an accommodation, please contact Disability Services, currently located in the Disability Services building at the Student Services at White Creek complex on west campus or call 979-845-1637. For additional information, visit http://disability.tamu.edu.

 

Schedule*

(Further minor changes may occur due to potential changes of the instructor’s schedule, and will be notified separately via email)

 

Week 1

 

08/28

1.     Introduction (i)

08/30

2.     Introduction (ii) [Slides 1+2]

Week 2

 

09/04

3.     Course Project Introduction [Walmart Overview] [Project Intro]

09/06

4.     Linear Algebra and Matrix Analysis (i)

Week 3

(Due by Week 3 Sunday: Decide Project Team Members)

09/11

5.     Linear Algebra and Matrix Analysis (ii) [Slides 3+4]

09/13

6.     Vector Space and Optimization (i)

Week 4

 

09/18

7.     Vector Space and Optimization (ii)

09/20

No Class (Scheduled Travel)

Week 5

 

09/25

8.     Vector Space and Optimization (iii) [Slides 5+6+7]

09/27

9.     Search and Planning (i)

Week 6

 

10/02

10.  Search and Planning (ii)

10/04

Midterm Exam (i)

Week 7

(Due by Week 7 Sunday: Submit Project Proposal)

10/09

11.  Constraint Satisfaction

10/11

12.  Game Theory (i)

Week 8

 

10/16

13.  Game Theory (ii)

10/18

14.  Probability

Week 9

 

10/23

15.  Bayesian Inference and Graphical Models (i)

10/25

16.  Bayesian Inference and Graphical Models (ii)

Week 10

 

10/30

17.  Bayesian Inference and Graphical Models (iii)

11/01

18.  Machine Learning (i): Statistical Theory

Week 11

 

11/06

19.  Machine Learning (ii): Applied Models

11/08

20.  Machine Learning (iii): Current Advances

Week 12

 

11/13

Midterm Exam (ii)

11/15

21.  Reinforcement Learning

Week 13

 

11/20

(Reserved for Travel Flexibility)

11/22

No Class (Thanksgiving)

Week 14

 

11/27

Final Project Presentations (i)

11/29

Final Project Presentations (ii)

Week 15

 

12/04

Final Project Presentations (iii)