Artificial Intelligence
Spring 2016

Course Information


PROJECT is due on MAY 10 or ANYTIME BEFORE May 10
Please e-mail your results and short Project Description to Professor and TA
YOU CAN do the project alone or with your Presentation Group


New  Presentations slides posted


Please e-mail Presentation Evaluation PART 1 ( just opinion, do not  need to compare) and PART  2  to TA and Professor  1-2 days after Presentation
I will collect PART THREE  in class.

STUDENTS' PRESENTATIONS  start Thursday April 21
Presentations Schedule is posted

EXTRA CREDIT problems posted - point will add to your Midterm 1 - but only  up to 100pts total

I made changes to the Grading Components (and Syllabus) - please read it

I put is Project Description and  UPDATED Presentation Schedule- please check it

Predicate Resolution new  Lecture Notes in Downloads
Predicate Logic Chapters: Laws of Quantifiers, Skolemization in Downloads
Nilsson's  Resolution Handout  posted

Time:  Tuesday, Thursday,   7:00pm  - 8:20 pm

Place: JAVITS 109

Professor: Anita Wasilewska

208 CS Building; tel: 632-8458
e-mail: anita@cs.stonybrook.edu
Office Hours: Tue, Th 2:30 - 3:30 pm and by appointment

Teaching Assistant

  TA: Ali Selman Aydin
  • TA e-mail:  aaydin@cs.stonybrook.edu
  • Office hours:    MONDAY  4pm- 6pm and by appointment
  • Office Location:  Room 2217 in 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 students' Research Presentations based on newest research and applications.

    Main Book

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

    Course Content and Structure

  • The course is divided into three parts, the third one reserved for students presentations

  • PART 1 :  General  Inroduction to AI

  • We  will cover the following subjects included in  additional book (see Syllabus), course handouts and in
  • course  Lecture Notes posted 

  • 1. AI history and applications
    2. Knowledge Representation and Inference
    3. Short overview of Expert Systems Design and Technology
    4. Overview of Propositional and Predicate Logic; Predicate languages and basic Laws of Quantifiers
    5. Automated theorem proving 1: Propositional Resolution
    6. Automated theorem proving 2: Predicate Resolution
  • Midterm 1 - MARCH 22

  • PART 2: Machine Learning

  • We will use my own Lecture Notes based on the BOOK and I will also post the original  Book Slides as a reference

  • We will follow the DATA MINING book very closely and in particular we will cover the following chapters and subjects.
  • The order does not need to be sequential
  • Chapter 1.  Introduction. General overview: what is Data Mining, which data, what kinds of patterns can be mined
    Chapter 2.  Data preprocessing: data cleaning, data integration and transformation, data reduction, discretization and concept hierarchy generation
    Chapter 5. Mining Association Rules in transactional databases and Apriori Algorithm
    Chapter 6. Classification and prediction:
    1. Decision Tree Induction ID3, C4.5
    2. Neural Networks
    3. Bayesian Classification
    4. Classification based on Concepts from Association rule mining
    5. Genetic algorithms
    Chapter 7. Cluster Analysis
  • Midterm 2 - Tuesday, APRIL 19

  • PART 3 : Students Presentations
  • Presentations START  Thursday, APRIL 21

  • Student Information

    Grading Components

    During the  the semester you have to complete the following

    1. Two Mid-semester Tests for total of 200pt
    2. Research Presentation - 60pts
    3. Students Presentations evaluation reports - 10 pts
    4. Final Project - 30pts

    Final grade computation

    NONE of grades will be CURVED

    During the semester you can earn 300pts
    The grade will be determine in the following way: of earned points/3 = % grade.
    The % grade which is translated into 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


    Course SYLLABUS
    Short Syllabus Slides
    Laws of Quantifiers Chapter
    Skolemization Chapter; Introduction to Resolution
    Nilsson Book Handout
    Busse Book Handout
    Short REVIEW for Midterm 2

    Part 1 Lecture Notes

    L1.  Introduction 
    L2.  Predicate Logic- Introduction
    L3.  Propositional and Predicate Languages
    L4.  Propositional Resolution
    L5.  Predicate Resolution Introduction
    L6.  Rules Based Systems
    L7.  Short Review

    Part 2 Lecture Notes

    L1.  General Introduction to Learning
    L2.  Chapter1: Introduction
    L3.  Chapter 2: Preprocessing  Long
    L3a.  Chapter 2: Preprocessing  Short
    L4.  Chapter 6: Classification Introduction
    L5.  Chapter 6: Classification Testing
    L6.  Decision Trees Introduction Introduction
    L7.   Decision Trees Full Algorithm
    L8.   Neural Networks
    L9.  GeneticAlgorithms
    L10.  GA Simple Examples
    L11.  Association Analysis
    L12.  Classification by Association
    L13.  Midterm 2 REVIEW 1
    L14.  Midterm 2 REVIEW 2

    Data Mining Book Slides

    Here is a full set of Book Second Edition Slides


    Datasets for data mining and knowledge discovery
    Datasets for data mining competitions
    University California Irvine KDD Archive
    World Bank datasets

    Students Presentations

  • Check 'Possible Presentations Subjects in the Syllabus to get an idea of the subjects to choose from.
  • Please mail Professor and the TA the number of members in the group, the name, E-mails and the SB Id of each group member, along with the possible  subject of the presentation. You CAN change the subject later
  • We will try to coordinate subject choices
  • Students CAN present the same subject but in this case MUST collaborate.
  • You can use any sources but use the terminology developed in Professor Lecture Notes and in the Book

  • Each student has to deliver a 30 minutes long presentation on a chosen topic of AI as a member of a chosen Presentation Team of two or three students
  • It is students responsibility to form the Presentation Teams
  • Each team has to have a designated Team Leader in order to communicate with Professor and the course TA
  • Please e-mail TA as soon as possible, and the latest by APRIL 4 th following:
  • names and e-mails of your Team members denoting who is the designated Team Leader
  • e-mail TA and Professor a TITLE and a one short paragraph long description of your team presentation;
  • you can CHANGE the subject  later, if needed
  • TA will assign a Team Number to each team and e- mail it to each  Team Leader   to be used for future correspondence
  • You have to use your Team Number wh reserving the presentation date; you don't need the TITLE to reserve the  presentation day

  • RESERVE the Presentation Date via e-mail to TA as soon as possible; "first come first serve"
  • Presentations SCHEDULE

  • D1   Thursday, APRIL 21
  • GROUP 10
    Outlier Analysis
  • GROUP 2
    Data Visualization
  • D2   Tuesday, APRIL 26
  • GROUP 3
    Web Mining
  • GROUP 4
    Genetic Algorithms
  • GROUP 5
    Predicting Future with Social Media
  • D3   Thursday, APRIL 28
  • GROUP 1
    The Renaissans NN
  • GROUP 6
    Game GO and NNetworks
  • GROUP 7
    Cluster Analysis 1
  • D4   Tuesday, MAY 3
  • GROUP 13:
    Prediction by Regresion
  • GROUP 8
    Text Mining 1
  • GROUP 9
    Modular NN
  • D5   Thursday, MAY 5
  • GROUP 11
    Natural Language Processing
  • GROUP 12
    Recurrent NN
  • GROUP 14
    Cluster Analysis 2
    METACLASSIFIER for Protein Secondary Structure Prediction
  • Students' Presentations Evaluation Report:

    Download a pdf of the report form from here


  • Project Data: - play around with the project data and familiarize yourself with it
  • bakarydata.xls
  • Project Description
  • Project Description SLIDES

    Project Tools


    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