CS6364 Artificial Intelligence

Instructor	Feng Chen
Office	ECSS 3.901
Number	(518) 442-4270
Email	feng.chen@utdallas.edu
Office Hour	Wednesday 2:00PM to 3:00PM Thursday 2:00PM to 3:00PM

TA	Chen Zhao
Office	ECSS 2.114
Number
Email	Chen.Zhao@utdallas.edu
Office Hour	2-3pm Thursdays and 10-11am Fridays

Class Time and Location

4:00PM-5:15PM, SOM 2.107

Course Description:

A course in artificial intelligence (AI) introducing basic concepts and techniques. Topics include statistics, optimization, first-order logic, probabilistic soft logic, Markov model and hidden Markov models, Markov random fields, and Artificial Neural networks.

Textbooks & References

There is no required textbook, but the following books may serve as useful references for different parts of the course.

Artificial Intelligence - A Modern Approach (AI) by Stuart Russell Peter Norvig Stuart J Russell
Pattern Recognition and Machine Learning (PRML) by Christopher M. Bishop
Machine Learning (ML) by Tom Mitchell
Deep Learning (DL) by Ian Goodfellow and Yoshua Bengio and Aaron Courville
To be added

Course Description:

Part	Lecture	Lecture Topics	Reading Materials
Introduction	1	Syllabus; Introduction	AI (Chapter 1); PRML (Chapter 1); DL (Chapter 1)
A: Statistics	2	Basic Distribution (Binomial, Poisson, Gaussian)	PRML (Chapter 2; Appendix B)
	3	Parameter Esitmation (Maximum Likelihood Estimation)	PRML (Chapter 2; Appendix B)
	4	Linear Regression, Logistic Regression	PRML (Section 3.1; Section 4.1) Par I and Part II in Andrew Ng's Lecture Notes Lectures 2.1 to 4.6 and Lectures 6.1 to 7.4 in Andrew NG's short videos
B: Numerical Optimization Techniques	5	Gradient Descent, Stochastic Gradient Descent, Mini-bach Gradient Descent, Momentum, Nesterov Momentum, AdaGrad, RMSProp, Adam	DL (Chapter 8) Lecture 17.1 to 17.4 in Andrew NG's short videos The Evolution of Gradient Descent How to implement linear regression using graident descent Interpretation of bias correction in the Adam algorithm
G: Artificial Neural Networks	6	Introduction: neurons, activiation function, artification, loss function, algorithms	DL (Chapter 6, 7) Introduciton to Deep Learning Part 1 Introduciton to Deep Learning Part 2 < Activiation Functions < Why Non-linear Activiation Functions Train/Dev/Test Sets Parameters vs Hyperparameters Hyperparameter Tunning in Practice Nuts and Bolts of Applying Deep Learning Improving deep neural networks: hyperparameter tuning, regularization and optimization Oneline demo of NN
	7	Convoluational Neural Networks	DL (Chapter 9) Lecture by Andrej Karpathy A short course on Convolutional Neural Networks by Anrew NG
	8	Deep Reinforcement Learning	Lecture by Serena Yeung Applications of Deep Reinforcement Learning Demo of Deep Reinforcement Learning
C: First Order Logic	9	Syntax and Semantics Learning and Inference
D: Probablistic Soft Logic	9	Syntax and Semantics Learning and Inference
E: Markov Model and Hidden Markov Model	10	TBD
F: Markov Random Fields	10	TBD
F: Markov Random Fields	9	TBD

Examinations and Grading:

Grading Distribution:

Midterm Exam: 30%
Class Discussion and Participation: 10%
Homework Assignment: 30% + Final Project (at most two-member team): 30%

Homework Requirement:

Correctly answer 90% of questions
Your first submission must answer at least 30% of the questions correctly.
After your first submission, you have two chances to make corrections of your homework answers.

Class Attendance

Class attendance is required and checked. Each case of missing class without a proper explanation will cause 20% less from your final numerical grade

Homework

Homework Assignments

Assignments will be due at the beginning of the class on the due date
All assignments must have your name, student ID, course name/number, and section number
File Name:

HW#_Name.pdf (writeup)
HW#_Name.zip (code)

Submit HW on the blackboard

Late Homework

30% penalty for the first 24-hour
70% penalty for >= 24 hour
Zero score for >= 3 days

Course Project Requirement

Timeline

Project presentation: (the class session time in the exam week)
Report submission deadline: Friday in the exam week

Project Report

IEEE two-column style format, Times New Roman, Font 10.
Each student should submit a separate report that should be 6 pages or more.
Useful links: Writing Technical Articles, Writing a Technical Report, Paper Writing and Presentations
The project report may contain the following sections:

Author (name, student ID, E-mail, and department)
Abstract
Introduction (including motivation, problem statement, significance of the problem)
Related work
Proposed approaches
System design and implementation (including architecture, datasets, major components, graphic user interface)
References

Example project reports

Example project report 1

Example project report 2

Example project report 3

Example project report 4

Example project report 5

Policy on Cheating:

Cheating in an exam will result in an E grade for the course. Further, the students involved will be referred to the Dean's o ce for disciplinary action.

Homework problems are meant to be individual exercises; you must do these by yourself. Any of the following actions will be considered as cheating.

A solution which is identical to or nearly identical to the solution submitted by another student in the class.
A solution which is identical to or nearly identical to the solution provided by the instructor in a previous offering of the course.
A solution which is identical to or nearly identical to a solution available on the Internet.

Cheating in a homework exercise will result in the following penalty for all the students involved.

The homework in which cheating occurred will be assigned a grade of ZERO.
The homework in which cheating occurred will be assigned a grade of ZERO.

Students who cheat in two or more homework assignments will receive an E grade for the course. The names of such students will also be forwarded to the Dean's office for disciplinary action.

For a complete list of UTD policies and procedures, see here .