Artificial Intelligence

CSE 5290/4301
Florida Institute of Technology
Instructor: Debasis Mitra


Department: Computer Sciences

Abstract

Artificial Intelligence or AI possibly generates more curiosity than any other sub-topic of computer science. In another sense AI is THE purpose of computer science! Why? Because, "computing" or doing arithmetic was dreamt of as one of early "intelligent" behaviors that may be mechanized. When it became clear that many other human activities may be mapped to arithmetic, that the early computers were so efficient with, the term AI was coined (1956). Since then a sub-topic within CS has shaped up. In this course we will make a short journey through those materials. After finishing this course, hopefully, you will be (1) knowledgeable enough to learn more if and when you need it, (2) join a workforce where some form of AI is deployed, and (3) demystify AI to your significant other!



The syllabus is: here,

Comps syllabus, Graduate students are responsible to study topics of comprehensive exam even if that is not covered in the class.

Florida Tech Student Handbook on Cheating and Paligiarism: http://www.fit.edu/studenthandbook/print.php#policy_2490

Pre-requisite knowledge:
(1) Discrete Mathematics, (2) Data structures and algorithms, and (3) Programming in a higher level language.

WARNING:
Disclaimers to the lecture notes:
THESE NOTES ARE FOR HELPING YOU TO STUDY THE TEXT BOOK. I KEEP UPDATING THESE AND WILL NOT BE RESPONSIBLE IF YOU FIND THAT THE NOTES HAVE CHANGED AFTER YOU HAVE LAST VIEWED IT. I TRY TO CHANGE THE ASSOCIATED DATE WHEN I MAKE THE LAST UPDATE.

+++++++++++++++++++ Lecture Notes +++++++++++++
Online text book site

Figures
Textbook slides , change chapter number on url

Introduction on AI and Background in CS
Problem Solving with Search, and Search algorithms in Text , Local Search algorithms in Text ,
Game Search:
My Notes, and from old Text.
A background material on NP-completeness
Reasoning with Constraints, and from the text, examples from Dechter's book, a PC example , and constraints counter examples from Edward's book,
A bit more animated lecture slides on constraints.
Spatio-temporal constraints
Automated Reasoning with Propositional Logic
Automated Reasoning with Predicate Logic, Problem with Existential Quantifire with implication , Sample Knowledge base
Sample Prolog:
resources
Inferencing in Predicate Logic
Modeling Uncertainty
Reasoning with Uncertainty, Part-II
Inferencing in probabilistic network, Part-II (we may not cover this part-2)
Machine learning-I
More on learning , from Ch 18 (18.6 onwards)
Machine learning with neural networks
AI and ethics: My thoughts, read also Chapter 26.3, 3rd ed. AIMA text

Statistical Machine learning I
Machine learning with neural networks
Classical Planning

-------------- Resources:
A discrete math online book from Dartmout: https://math.dartmouth.edu/archive/m19w03/public_html/book.html
Artificial Intelligence IS Computer Science! Turing's Imitation game-paper, 1950 (20pg).
First workshop Proposing the term AI, 1955.
Open letter on AI by Stephen Hawking, Elen Musk and others.
A paper on AI and ethics by Bostrom-Yudkowsky (21 pages).
US Govt. Strategic Plan, 2016.
Hidden Markov Model: a conoical tutorial by Lawrence Rabiner, 1988 (30pg).

===================== Fall 2017 =================
Class 6:30 pm - 7:45 pm TR Frederick C. Crawford Bldg 220

Continuously updated schedule of: CSE 4301/5290 , for Fall 2017.

I have found an 8-puzzle resource from a question by someone in the class, here it is
Project:
ideas for Fall 2017. I will flesh out details discussing with you.

<<<<<<<<<<<<<<<<<<<< Spring 2017 >>>>>>>>>>>>>>>>>>>>>>
CSE5290: CRF 401, TR 8-9:15 pm

Continuously updated schedule of: CSE 4301/5290 , for Spring 2017.

Quiz1 search: key is here.

Quiz2 on constraints: is here.

Key to last exam/quiz on Prob. Reasoning, Mach. learning and AI ethics: is here.

Project Reports with my comments on it: Watson Analytics.
N-gram on Indus Valley Scripts.
Bayesian Clustering.
Support Vector Machine.
N-gram on English.
Conv. Neur. Net for Semi-supervised Learning.
Conv. Neur. Net.

<<<<<<<<<<<<<<<<<<<< Fall 2016 >>>>>>>>>>>>>>>>>>>>>>
CSE4301/5290: CRF 220, TR 6:30-7:45 pm

Continuously updated schedule of: CSE 4301/5290 , for Fall 2016.

Home Work-1 here , due 9/27/Tuesday at the begining of the class in hard copy, key will be here.

Home Work-2 [Hw2: Text Edition 3, Exc. 8.6, 8.9, 8.23 Grad std special: create CNF of 8.9.b.(i), 8.9.d.(iii)] Q8.6 , Q8.9 , Q8.23 , due 10/13/Thursday at the begining of the class in hard copy.

Home Work-3, Prob-Reasoning hints

Home Work-4, Machine learning with hints.

Key to the , Final
I will be out of the country shortly, see you next semester.
Undergrad project , description is here.

Graduate Projects' instructions
Theory presentations below:
ICA-CNN , Liu-Gui (Team 1) notes
CNN-SVM, Tan-Ballard (2) notes
PCA-LDA , Lobo-Shreya (3) notes
PCA-CNN , Zongqiao-Wei (4) report1 and report2
CNN-FA , Mike-Chris (5) notes
FA-NMF , Zubin-Taher (6) notes on FA-NMF
PCA-ICA , Chmiao-Zemeng (7) notes

Undergrad project-2 , description is here.

URGENT: UG PROJECT-2 REPORTS: IF YOU DO NOT SEE CORRECT GRADE ON THE SPREAD-SHEET, THEN SCAN YOUR PROJECT'S FIRST PAGE AND E-MAIL ME
Grade Spreadsheet: here

Materials are copyrighted to me (year 2016). Many materials have been developed before I joined FIT. E-mail: dmitra@cs.fit.edu