Florida Institute of Technology

Instructor: Debasis Mitra

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*