Class description:  Principles of artificial intelligence. Uninformed and informed search. Constraint satisfaction. AI for game playing. Probabilistic reasoning, Markov decision processes, hidden Markov models, Bayes nets. Neural networks and deep learning. 
Course objectives:  By the end of the semester each student will be able to:

Instructor:  Dr. Lotzi Bölöni 
Office:  HEC  319 
Phone:  (407) 2438256 (on last resort) 
Email:  lboloni@cs.ucf.edu (preferred means of communication) 
Web Site: 
http://www.cs.ucf.edu/~lboloni/Teaching/CAP5636_Fall2017/index.html
The assignments and the other announcements will be posted on the course web site 
Classroom:  ENG2 203 
Class Hours:  Tue, Th 4:30PM  5:45PM 
Office Hours:  Tue, Th 6:00PM  7:30PM 
Prerequisites:  The class does not depend on the undergraduate AI class, although some concepts might be easier if you have seen them before. The projects require Python programming. If you can program in any programming language (C/C++/Java/Javascript) you should be ok. 
Required texts:  There is no required textbook. 
Recommended readings: 

Grading: 

Integrity:  The department, college, and University are committed to honesty and integrity in all academic matters. We do not tolerate academic misconduct by students in any form, including cheating, plagiarism and commercial use of academic materials.
Please consult the Golden Rule
Handbook for the procedures which will be applied. 
Verification of engagement:  As of Fall 2014, all faculty members are required to document students' academic activity at the beginning of each course. In order to document that you began this course, please complete the following academic activity by the end of the
first week of classes, or as soon as possible after adding the course, but no later than August 27. Failure to do so will result in a delay in the disbursement of your financial aid. To satisfy this requirement, you must finish the first quiz posted online. Log in to Webcourses, choose CAP 5636, and submit your answers online. 
Date 
Topic 
Lecture Notes, Readings, Homeworks 
Aug. 22 
History and positioning of AI 
[slides]
History and positioning of AI 
Aug. 24 
Uninformed search

[slides] Uninformed search 
Aug. 29 
Informed search: A* search and heuristics

[slides] Informed search Homework 1: Project 1 from the Berkeley AI class. Due Points are worth as follows: Q1..Q4 25 points each, Q5..A8 10 points each. Total achievable points 140 points. 
Aug. 31 

Sept. 5 


Mon, Sept. 7 
UCF closed due to Hurricane Irma 

Mon, Sept. 12 
UCF closed due to Hurricane Irma 

Mon, Sept. 14 
UCF closed due to Hurricane Irma 

Sept. 19 
Game playing and adversarial search

[slides] Adversarial search 
Sept. 21  Expectimax search and utilities

[slides] Expectimax search and utilities 
Sept. 26 
Markov decision processes 1

[slides] Markov Decision Processes 1 
Sept. 28 
Markov decision processes 2

[slides] Markov Decision Processes 2 
Oct. 3 
Reinforcement learning 1

[slides]
Reinforcement learning 1 
Oct. 5 


Oct. 10 
Reinforcement learning 2

[slides]
Reinforcement learning 2 
Oct. 12 
Midterm 1: from introduction to Markov Decision Processes (inclusive)  
Oct. 17 
Probability

[slides] Probability 
Oct. 19 


Oct. 24 
Markov models

[slides] Markov models 
Oct. 26 
Hidden Markov models

[slides] Hidden
Markov models 
Oct. 31 
Particle filters and applications of HMMs

[slides] Particle filters and Applications of HMMs 
Nov. 2 


Nov. 7 
Classification, principles of machine learning,
naive Bayes

[slides] Classification and naive Bayes 
Nov. 9 
Neural networks  perceptron

[slides] Perceptron 
Nov. 14 
Midterm exam 2: From reinforcement learning to
particle filters (inclusive) 

Nov. 16 
Casebased reasoning, kernels and clustering

[slides]
Kernels and clustering 
Nov. 21 
Deep learning 

Nov. 23 
Thanksgiving break  
Nov. 28 
Deep learning 2: Long short term memory 

Nov. 30 
Artificial General Intelligence 1.


Final exam Thursday, December 07, 2017, 4:00 PM  6:50 PM 