CSE352
ARTIFICIAL INTELLIGENCE
FALL 2019



Course Information

News:  

SHORT  REVIEW for FINAL  POSTED

ASSOCIATION Review  POSTED

FINAL COVERS subjects: Logic, Resolution, Building  Decision Tree Classifier, Appriori Algorithm and
Classification by Association

Thursday, December 5 -  Review FOR FINAL

Q2  SOLUTIONS posted 

FINAL
December  17   11:15 am - 1:45 pm  JAVITS 111


MIDTERM
solutions POSTED
MIDTERM 
RETURN  and CONSULTATION DAY

Monday, November 18,  1:00 am - 3:00 pm 
in Old CS Building Room# 2217

Tuesday, November 19,  1:00 am - 3:00 pm  in Old CS Building Room# 2217

Students can discuss GRADING with TAs only during these 2 days and only when they pick up the TEST
They can't take it home and come back and then discuss it
 ONCE the test  is taken from TAs office - end of discussion
 TAs can ALTER the grades - if there is a reason- but ONLY during these two meeting
 In cases of serious disagreements TA will consult the grade with me

 REMEMBER: NO DISCUSSION of grades after these 2 consultation days
 

PROJECT Hmk   is due Thursday, November 14 (midnight)  via Blackboard

PROJECT due DATE moved  to NOVEMBER 26 (midnight)  via Blackboard

FINAL
December  17   11:15 am - 1:45 pm  JAVITS 111


IT is time to think about the TEAM PROJECT  and teams formation

Here is the Teams Formation Procedure
 
Please e-mail TA TAO SUN names and e-mails of your Team members denoting who is the designated Team Leader
TA will assign a Team Number to each team and e- mail it to each Team Leader to be used for future correspondence.

CONTACT  TA TAO SUN  if you DO NOT HAVE a team partner
He will help you to FORM A TEAM

All this has to be done by November 10  as the Project Data (extra credit ) are due November 14 and
the PROJECT is due NOVEMBER 26 - midnight


TAs  OFFICE HOURS POSTED

TESTS and Quizzes POLICY

We do not give make-ups  except of documented cases of illness or documented emergencies

 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 test when you take them


Time:  Tuesday,  Thursday   2:30 - 3:50 pm

Place: JAVITS 111

 Professor:   Anita Wasilewska

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

Office Hours:
Tuesday, Thursday  4:30 pm - 6:00 pm,  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:   Tao Sun

e-mail:  tao@cs.stonybrook.edu
Office Hours: Monday 3:00 pm - 5:00pm
Office Location:   2217 Old CS Building

TA:  Ishika Agarwal

e-mail:  iagarwal@cs.stonybrook.edu
Office Hours: Friday 2:00 pm - 3:00pm
Office Location:   2217 Old CS Building

TA:  Omik Gokul Mahajan

e-mail:  omahajan@cs.stonybrook.edu
Office Hours: Monday 1:00 pm - 2 :00 pm
Office Location:   2217 Old 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 lectures based on newest research and applications.

Book1

The Essence of Artificial Intelligence
Allison Cawsey
Prentice Hall, 1998


This is a short, not expensive book but not very technical.
We will cover in detail first 3 chapters  and Chapter 5 supplemented by the  Lecture Notes for technical details)
The chapter 7  is supplemented by the Book 2

Course LECTURE NOTES posted in Downloads EXTEND  the material from the book providing TECHNICAL details and are the MAJOR SOURCE for the course.

Book 2

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


Data Mining
(DM) now also called also BIG DATA is a  multidisciplinary field.
It brings together research and ideas from database technology, machine learning, neural networks, statistics, pattern recognition,  knowledge based systems, information retrieval, high-performance computing, and data visualization.
 Its main focus is the automated extraction of patterns representing knowledge implicitly stored in large databases,
data warehouses, and other massive information repositories.

Lectures 7-20   cover parts or all of the Machine Learning Algorithms included in  CHAPTERS 6, 7 and CHAPTERS 8, 9
of the Book 2

Do not need to buy the book. You can download detailed slides for Third Edition at  DM SLIDES

Additional Book

LOGICS FOR COMPUTER SCIENCE: Classical  and Non - Classical
Anita Wasilewska
Springer,  2018

We use (Lectures 4, 6)  parts of CHAPTERS 2 and 6

CHAPTER 2: Introduction to Classical Logic

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.

AI talks in the Department

tba

Grading

During the semester you have to complete the following
TWO QUIZZES  -  given in class, 15pts each -  TOTAL 30pts
No make-ups

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
It is a TEAM project - see the Team Formation section for explanation

FINAL  65pts
 
Extra Credit
You can earn up to 10 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 

Tests Schedule
It is a preliminary schedule; changes, if needed will be advertised in  News

Q1   Thursday,  October 10
Fall Break   October 14 -15
MIDTERM    Thursday,  November 7
Project Data Preparation (10 extra points)
Submit it via Blackboard any day before or on Thursday, November 14
 Thanksgiving Break     November 21-25
PROJECT   submitt   it via  Blackbord  any day before  or on Wednesday,  November 26
Q2
   Tuesday,  December 3
 FINAL  
given during Finals Time December 11-19

PROJECT

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

Project Description
Write a detailed  Project Report explaining all methods used, motivations,  experiments results and their  comparison  Submitt it via Blackboard
Project Data Preparation Description
(for extra credit)

Project Tools

WEKA
RapidMiner
 
ATTENTION 
Project has to be to be conducted in Teams  of  3 - 4 students
All members of the Team receive the same grade

 TEAMS FORMATION

Please e-mail TA   TAO SUN  names and e-mails of your Team members denoting  who is the designated Team Leader
TA will assign a Team Number to each team and e- mail it to each  Team Leader   to be used for future correspondence.
Contact  TA  if you do not HAVE a team partner. He will help you to FORM A TEAM

 All this has to be done  by November 10  as  the PROJECT  is   due  November 20 - midnight

 

DOWNLOADS

SHORT REVIEW FOR FINAL
 
ASSOCIATION REVIEW
 

Q2 SOLUTIONS
 
MIDTERM SOLUTIONS
 
Q1 SOLUTIONS
 
MIDTERM REVIEW
 
Syllabus
Course Syllabus Slides
Project Description Slides

HANDOUTS

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


Homeworks

NONE of Homeworks 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 - Additional Book, Chapter 2
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 14a: Building a Classifier 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

Extra Lectures - Presentations- for READING

Bayes 1
Bayes 2
Data Warehouse
Genetic Algorithms Applications
Image Classification
NLP Models
Natural Language Processing
Opinion Mining
Clustering 1
Clustering 2
Regression 1
Regression 2
Regression 3
Text Mining 1
Text Mining 2
Web Mining 1
Web Mining 2

Some Applications of AI Presentations for READING

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