Course Information


THANK YOU for being such good Students!

Have  great FINALS and Summer!

FINAL  - is scheduled  for Monday, MAY 16, 5:30pm - 8:00pm, LT ENGR 102



is given in class on Thursday, APRIL 28
Q2 covers Neural Networks Design, Principles and Backpropagation Algorithm (Lecture 13),
 Building a Classifier Principles and Steps,  Basic Decision Tree algorithm and Review Lectures 14 -14a

Updated TESTS Schedule:
Project Data Preparation - already posted in Assignments and due  APRIL 5
MIDTERM  -  given in class on  April 12
Q2 - given in class on April 28
PROJECT - will be posted in Assignments and due  APRIL 21

FINAL  - is scheduled  for Monday, MAY 16, 5:30pm - 8:00pm, LT ENGR 102

Q1 Solutions Posted

Q1 is  given  MARCH 8   at  4:45 pm, in class
Q1 covers material from Lectures  1 - 6, Homework1 and Resolution Homework
Q1  (15pts + 5 extra points) is designed as your Practice  Test and you will be given  the whole class time
 (instead 35 minutes) to complete it. There will be 5 Problems similar to Homework 1 and Resolution Homework.
Midterm  (April 7) will have additional Problems covering  Lectures 7 -12


Time:  Tuesday,  Thursday   4:45pm - 6:05 pm


 Professor:   Anita Wasilewska

208 New Computer Science Building
Phone: 632-8458
e-mail: anitaatcs.stonybrook.edu 

Professor Office Hours
Tuesday, Thursday  6:15pm - 7:30pm, and  by appointment
New Computer Science Building, Room 2
I also read emails daily and respond within a day or two to students' emails


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

TAs Office Hours

TA:   Mohib Azam

e-mail: mohib.azam@stonybrook.edu
Office Hours: Wednesday 6:00pm - 7:00pm, Friday 12:00pm - 1:00pm
Office Location:   2126 Old CS Building

TA:   Mayank Mayank

e-mail: mmayank@cs.stonybrook.edu
Office Hours: Tuesday, Thursday 3:00pm - 4:00pm
Office Location:   2126 Old CS Building

TA:   Swapnil Satish More

e-mail: ismore@cs.stonybrook.edu
Office Hours: Tuesday, Thursday 2:00pm - 3:00pm
Office Location:   2126 Old CS Building

TA:   Ishaan Ballal

e-mail: iballal@cs.stonybrook.edu
Office Hours: Wednesday 3:00pm - 4:00pm, Thursday 4:00pm- 5:00pm
Office Location:   2126 Old CS Building

TA:   Gopi Sumanth Sanka

e-mail: gsanka@cs.stonybrook.edu
Office Hours: Monday 4:30pm - 5:30pm, Thursday 6:30pm - 7:30pm
Office Location:   2126 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.


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



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 15 pts of extra credit points during the semester

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
DO NOT SUBMIT Homeworks Solutions

Final Grade Computation


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 

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

Q1   March 8  in class
Spring Break   March 14 - 20
Project Data Preparation (10 extra points) Submit it via Blackboard any day before or on April 5
MIDTERM     April  12
Q2  April 21
Project   submit   it via  Blackboard  any day before  or on April 19
Last Class  -  May 5 --Review for Final
- scheduled Monday, MAY16,  5:30pm - 8:00pm


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

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

Please e-mail TAs 
 ISHAAN BALLAL  iballal@cs.stonybrook.edu  or   GOPI SUMMANTH SANKA   gsanka@cs.stonybrook.edu
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  TAs  if you do not HAVE a team partner. He will help you to FORM A TEAM

 All this has to be done before the SPRING BREAK  by MARCH 11  
Preparation (extra Credit) is due April 5



Course Syllabus Slides
Project Description Slides



Nilsson AI Book Introduction HANDOUT
BUSSE HANDOUT for Rules Based Systems
Nilsson AI Book Resolution HANDOUT
Nilsson AI Book Propositional Resolution Strategies HANDOUT
 Decision Tree and NN Algorithms HANDOUT


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

Homework 1
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 7: Chapter 7; Introduction to Learning
Lecture 8: Classification, Part 1: Introduction
Lecture 9: Classification Part 2: Testing Classifier Accuracy
Lecture 10: Preprocessing
Lecture 11: Decision Tree BASIC Algoirithm, Examples
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
Resolution (old)
Resolution Strategies (old)
Predicate Logic, Part 2

Extra Lectures - Presentations- for READING

Bayes 1
Bayes 2
Data Warehouse
Genetic Algorithms Applications
NLP Models
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 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


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