CSE352
ARTIFICIAL INTELLIGENCE
FALL 2018



Course Information

News: 

MIDTERM:  Wednesday, November 7, in class

Midterm Short Review POSTED

REVIEW for MIDTERM
 

FINAL is scheduled for Wednesday DECEMBER 12, 5:30 - 8:00 pm

 Q1 SOLUTIONS POSTED


Q1 will contain 3-4 short QUESTIONS and last about 35 minutes

Q1 covers  Lectures 1 - 6
Resolution Lecture  only Part1 and Part 2
You must know basic definitions and be able to solve EXAMPLES from Lectures

In particular, you must know examples from Lecture 4: LOGIC REVIEW

Q1  ALSO covers problems from Homework 1 and Resolution Hmk Part 1 and Part 2

Lecture 6: RESOLUTION
POSTED  RESOLUTION HOMEWORK

Finished  Lectures 5, 5a RULES BASED SYSTEMS


NEW LECTURE: LOGIC REVIEW is POSTED
READ LOGIC Chapter in Downloads

TAs  office hours posted

FALL BREAK is  October 8-9



Time:  Monday,  Wednesday     2:30 - 3:50 pm

Place: JAVITS 109

 Professor:   Anita Wasilewska

208 NCS Building
Phone: 632-8458
e-mail: anitaatcs.stonybrook.edu 

Office Hours:
Monday, Wednesday  12:40 pm - 2:00 pm, 
Wednesday, 7:15pm - 8pm,   and  by appointment

Teaching Assistants

ALL GRADES are listed on BLACKBOARD
Contact TAs if you need more information or need to talk about grading
We will list names who is correcting which  part of the test when you take them

We have very good TAs - please e-mail them, go to see them anytime  you need help

TA: Shihao Zhou

e-mail:  shzzhou@cs.stonybrook.edu
Office Hours: Tuesday, Thursday, 4:00pm- 5:00 pm
Office Location:   2217 Old CS Building

TA: Harsh Gupta

e-mail:  hagupta@cs.stonybrook.edu
Office Hours: Tueday, 12:00 pm - 1:00 pm  and   Wednesday, 12:30 pm -   1:30pm
Office Location:   336 (PACE lab) in  NEW CS Building

TA: Ashish Yadav

e-mail: ashyadav@cs.stonybrook.edu
Office Hours: TBA
Office Location:   2217 Old CS Building

TA: Manhas, Robin

e-mail: nhas@cs.stonybrook.edu
Office Hours:  Tuesday, 11:00 am - 12:00 pm and Wednesday, 11:30 am - 12:30 pm
Office Location:   336 PACE LAB, NEW CS Building


General Course Description

Artificial Intelligence is a broad and well established field and AI textbooks seem to be getting longer and longer and and often narrowly specialized. Our course attempts t to provide a concise and accessible introduction to the field. It is designed to give a broad, yet in-depth overview of different fields of AI. We will examine the most recognized techniques and algorithms in a rigorous detail. We will also explore trends, areas, and developments of the field in form of students' Research Presentations based on newest research and applications.

Main Book

The Essence of Artificial Intelligence
Allison Cawsey
Prentice Hall, 1998


This is a short condensed book (not expensive!) and not very technical.
We will cover in detail first 3 chapters (plus my lecture notes for technical details) and chapter 7
Course LECTURE NOTES posted in Downloads EXTEND  the material from the book providing TECHNICAL details and are the MAJOR SOURCE for the course.

Additional Book:

DATA MINING Concepts and Techniques
Jiawei Han, Micheline Kamber
Morgan Kaufman Publishers, 2003, 2011


The course outcomes and catalog description are in the official course description page

Student Information

Students ATTENDANCE is the most important, as Lecture Notes serve as an extra textbook for the course and students presentations are integral and as important part of the course design as Professor's lecture.

 I will check class attendance by giving and collecting answers (almost each class) to small questions connected with the lecture; you will get 1-2 extra credit points for your answers

AI talks in the Department

tba

Grading

During the semester you have to complete the following

TWO QUIZZES  - 15pts each -  TOTAL 30pts

MIDTERM  65pts   given in class  

PROJECT (it is  an application project- not programming)  - 40 pts
 See the section PROJECT below and Project Description in the Syllabus

FINAL  65pts   

Extra Credit: I will give during the class small questions for extra credit and assign some extra credit work

You can earn up to 20 pts of extra credit points during the semester

HOMEWORKS
I posted 4 Homework Assignments AND Homework Solutions
NONE will be collected nor graded- they are posted for you do study from them
I encourage students to SOLVE homework problems first- and then to compare their solutions with those posted Quizzes and TESTS will contain problems very similar to the Homework Problems
I will be posting some additional HMKs during the semester

Final Grade Computation

NONE OF THE GRADES WILL BE CURVED

During the semester you can earn 200pts plus extra credit pts

The grade will be determined in the following way: number of earned points divided by 2 = % grade
 
The % grade is translated into a letter grade in a standard way  i.e.

100 - 90 % is A range,        89 - 80 % is B range,

79 - 70 % is C range,            69 - 60 % is D range,

and F is below 60%
 

See course SYLLABUS for details

Test Schedule

It is a preliminary schedule; changes, if needed will be advertised in this section

Q1   Monday,  October 15
MIDTERM    Wednesday,  November 7
 PROJECT Data Preparation(10 extra points) is due Wednesday, November 20 or any date before
 Thanksgiving Break    November 21-25
PROJECT due Wednesday,  November 28
Q2
   Wednesday,  December 5
  FINAL 
December 12, 5:30pm - 8:00 pm - PLACE tba

Project Data

Play around with the data and familiarize yourself with it (DOWNLOAD: bakarydata.xl )

Project Description

Project Description
Project Data Preparation

Project Tools

WEKA
RapidMiner

Past Project Examples

Example 1
Example 2
Example 3

DOWNLOADS

REVIEW for MIDTERM
 

Q1 SOLUTIONS
 
Syllabus
Course Syllabus Slides
 
LOGIC Chapter: Introduction to Classical Logic 
 

HANDOUTS

BUSSE HANDOUT for Rules Based Systems
Resolution HANDOUT
 Decision Tree and NN Algorithms HANDOUT


HOMEWORKS

NONE will be collected nor graded- they are posted for you do study from them

Homework 1
RESOLUTION Homework
Homework 2
Homework 3
Homework 4

Homeworks Solutions

Homework 1 Solutions
RESOLUTION Homework Solutions
Homework 2 Solutions
Homework 3 Solutions

 Lecture Notes:

Lecture 1: Chapter 1; Introduction to AI
Lecture 2: Chapter 2; Knowledge Representation
Lecture 3: Chapter 2; Predicate Logic Part 1
Lecture 4: LOGIC REVIEW
Lecture 5: Chapters 2, 3, 4, Busse Notes: Rule Based Systems
Lecture 5a: REVIEW for Hmk 1
Lecture 6: RESOLUTION
Lecture 7: Chapter 7; Introduction to Learning
Lecture 8: Classification, Part 1: Introduction
Lecture 9: Classification Part 2: Testing Classifier Accuracy
Lecture 10: Decision Tree BASIC Algoirithm, Examples
Lecture 11: Preprocessing
Lecture 12: Decision Tree Full Algorithm
Lecture 13: Neural Networks
Lecture 14: Classification Review
Lecture 15: Association- Apriori Algorithm
Lecture 16: Classification by Association
Lecture 17: Genetic Algorithms
Lecture 18: Genetic Algorithms Examples
Lecture 19: Clustering
Lecture 20: Bayesian Classification
Resolution (old)
Resolution Strategies )old)
Predicate Logic, Part 2

Past Students Presentations

Games and AI
Applications of GA in AI
AI in Tabletop Games
AI within FINANCE
 AI in MUSIC
AI and Neural Networks
AI in Video Games 
AI in Self-driving Cars
 AI in Pattern Recognition
AI for Children
AI in Facial Recognition
Pattern Recognition
Sci-Fic Film Short Circuit
Google Deep Dream
AI in Voice and Image Recognition
Self Driving Cars
AI History
AI in Games
Sound Recognition

ACADEMIC INTEGRITY STATEMENT

Each student must pursue his or her academic goals honestly and be personally accountable for all submitted work. Representing another person's work as your own is always wrong. Any suspected instance of academic dishonesty will be reported to the Academic Judiciary. For more comprehensive information on academic integrity, including categories of academic dishonesty, please refer to the academic judiciary website at Academic Judiciary Website

Stony Brook University Syllabus Statement

If you have a physical, psychological, medical, or learning disability that may impact your course work, please contact Disability Support Services at (631) 632-6748 or Disability Support ServicesWebsite They will determine with you what accommodations are necessary and appropriate. All information and documentation is confidential. Students who require assistance during emergency evacuation are encouraged to discuss their needs with their professors and Disability Support Services. For procedures and information go to the following website: Disability Support Services Website