CSE 368: Intro To Artificial Intelligence

Instructor : Dr. Sreyasee Das Bhattacharjee

Email : sreyasee@buffalo.edu
Office: Davis Hall 349
Office Hours: F, 530pm to 630pm and By Appointment

General Information

Lectures: Tues, Thurs, 12:30pm-1:50pm

TA(s):TBD

Course Overview: Artificial Intelligence research has shown significant promise in automating the task of web search, speech recognition, face recognition, machine translation, autonomous driving, etc. While search, recognition are some of the tasks, sound trivial to human, understanding and simulating the processes that the human brain performs in order to execute these actions, is not straight forward. The goal of artificial intelligence is to tackle these complex real world problems with rigorous mathematical tools. In this course, we will learn the underlying principles that enable these applications and practice implementing some of these systems. Specific topics include various search algorithms, Markov decision processes, constraint satisfaction, graphical models, and machine learning. The course will help you learn about the latest tools to tackle new AI problems you might encounter in life.

Piazza : We will use Piazza to answer questions and post announcements about the course. Please sign up here.

Course Prerequisites: CSE 116 (Python Programming), EAS 305 or MTH 411 or STA 301

Syllabus : You can access the Syllabus here. 

Grade Composition :       Programming Assignments, 30%
                          Written Assignments, 20%
                          Class Activities/Self Assessment, 10%
                          Mid-term(s): 20%
                          Final: 20%

Reading Materials:
Artificial Intelligence: A Modern Approach, 2nd EditionStuart Russell, Peter Norvig, Google Inc.-(RN) Tom Mitchell, Machine Learning, McGraw-Hill, 1997

Other Supporting Materials (will continue to be extended later on):

Course Schedule

(Tentative, more Reading Materials to be added during the course)
Week Lecture Reading Materials
Week 1-2 Introduction, Uninformed Search Chapter 1, 3, RN
Week 2-3 Informed Search Chapter 4, RN
Week 4 Constrained Satisfaction Problem(CSP) Chapter 5, RN
Week 5-6 Advanced Search Chapter 6, RN
Week 7 Markov Decesion Process(MDP) Chapter 17, RN
Week 8 MIDTERM
Week 9 Reinforcement Learning Chapter 21, RN
Week 10-11 Probabilistic Reasoning Over Time Chapter 15, RN
Week 11-12 Uncertainty, Probabilistic Models Chapter 13, 14, RN
Week 13,14 Statistical Learning Models Chapter 14,15
Week 15 Review For Final

Academic Integrity:
(Short) Do not cheat! You will be caught and punished. Our department is serious about graduating ethical and upstanding computer scientists. The policy has recently been updated and will be enforced.
(Long) All academic work must be your own. Plagiarism, defined as copying or receiving materials from a source or sources and submitting this material as one's own without acknowledging the particular debts to the source (quotations, paraphrases, basic ideas), or otherwise representing the work of another as one’s own, is never allowed. Collaboration, usually evidenced by unjustifiable similarity, is never permitted in individual assignments. Any submitted academic work may be subject to screening by software programs designed to detect evidence of plagiarism or collaboration. Also, do not post any of the course material outside of the Course piazza page. It will be interpreted as an attempt to get non-approved help. For more info :
UB CSE Academic Integrity

Working with others: Please do help each other! This material is fun, but can be challenging. Discussing it with peers can deepen your understanding. You can talk about the homework problems and ways of approaching them, however, every person must write up solutions and code separately. We will compare all submissions with each other AND non-approved sources. I you can find something online, so can we.

Special Accommodations: In case of need of special accommodations please go the following link for more information.
Special Accommodations.