The CSE4301 syllabus is: here, and the CSE 5290 syllabus is here
The Graduate Comprehensive Exam's syllabus, Some topics for Graduate comprehensive exam may not be covered in the class.
A few samples: CompsAiSpr17, CompsAiSpr18,
Florida Tech Students' Handbook on Cheating and Paligiarism: http://www.fit.edu/studenthandbook/print.php#policy_2490
Prerequisites:
(1) Discrete Mathematics, (2) Data structures and algorithms, and
(3) Programming in a higher level language.
===================== Spring 2023 =================
Spring 2023 CLASS coordinates: 3:30 - 4:45 pm TR OlinLifeSciences129
Follow the "table" below.
Continuously updated: day to day schedule.
Graduate student project's introduction.
>>>>>>>>>>>>>>>>>>>>>>>>>>>>
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 +++++++++++++
Text book: S. Russell and P. Norvig, Artificial Intelligence: Modern Approach. Pearson, third ed., 2010. http://aima.cs.berkeley.edu/
Figures
Textbook slides , change chapter number on url
Introduction on AI and Background in CS
Complexity theory-lite (7% with surgeon general's warning, etc.)
Problem Solving with Search, and
Search algorithms in Text Ch-4, Local Search algorithms in Text ,
Game Search: My Notes, and from the text.
A background material on NP-completeness
Reasoning with Constraints, and from the
text Ch-5, examples from Dechter's book, a PC example , and constraints counter examples from Edward's book,
A bit more animated lecture slides on constraints,
Another movie animation on some constraint reasoning algorithms from Andrew Moore at CMU.
Spatio-temporal constraints
Automated Reasoning with Propositional Logic Ch-7,
DPLL
Automated Reasoning with Predicate Logic, Problem with Existential Quantifier with implication ,
Example of Skolemization and Existential Quantifier elimination
Sample Knowledge base on a book page
Inferencing in Predicate Logic
Sample Expert system code with CLIPS
Sample Prolog code
Sample automated reasoning code with Otter system
Modeling Uncertainty
I am jotting down some additional clarification notes on probabilistic reasoning.
Reasoning with Uncertainty, Part-II: Inferencing
Temporal probabilistic network, Part-II (we will not cover these two parts)
Machine learning-I
More on learning , from Ch 18 (my text notes 18.6 onwards)
Machine learning with neural networks
My slides on Machine learning (additional materials to above)
AI and ethics: My thoughts, read also Chapter 26.3, 3rd ed. AIMA text
Statistical Machine learning I
Classical Planning
| MODULE | TOPIC | TEXT CHAPTER(3rd ed) |
|---|---|---|
| Background | Introduction on AI and Background in CS | Read early chapters |
| SEARCH | Problem Solving with Search, | chapter04a.pdf, Local Search algorithms chapter04b.pdf |
| SEARCH | Adversarial Game Search, | chapter06.pdf |
| SEARCH | Constraints Reasoning, | chapter05.pdf |
| ========================== | ======================================= | ======================================= |
| LOGIC | Automated Reasoning with Propositional Logic | chapter07.pdf, Additional: DPLL |
| LOGIC | Automated Reasoning with Predicate Logic | chapter08.pdf, Inferencing: chapter09.pdf , Additional: Problem with Existential Quantifier with implication , Example of Skolemization and Existential Quantifier elimination |
| LOGIC | Sample Knowledge base on a book page Inferencing in Predicate Logic Sample Expert system code with CLIPS Sample Prolog code Sample automated reasoning code with Otter system |
None |
| ========================== | ======================================= | ======================================= |
| UNCERTAINTY | Probabilistic Reasoning | chapter13.pdf |
| Bayesian Network | chapter14a.pdf | |
| ========================== | ======================================= | ======================================= |
| MACHINE LEARNING | Basics + Decision Tree | chapter18.pdf |
-------------- Resources:
A recent (May'19) survey from MIT-Tech Reviews finds a slowing-down trend for deep learning .
A news item on US interest, 2019.
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).
Nice set of Data Science / ML interview questions
Materials are copyrighted to me (year 2019). Many materials
were developed before I joined FIT.
E-mail:
dmitra at cs.fit.edu
