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. 
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_Fall2016/index.html
The assignments and the other announcements will be posted on the course web site 
Classroom:  ENG1 0286 
Class Hours:  Tue, Th 4:30PM  5:45PM 
Office Hours:  Tue, Th 6:00PM  7:30PM 
Prerequisites:  Some programming experience. 
Textbook:  Russel & Norvig, 3rd edition 
Grading:  Homeworks: 25%, Quizzes: 5% Midterm 1: 20%, Midterm 2: 20%, Final: 30%. Grading formula: HW = (HW1 + HW2 + HW3 + ...+ HWn) / n Q = (Q1 + ... + Qn) / n Overall = 0.25 * HW + 0.05 * Q + 0.2 * M1 + 0.2 * M2 + 0.3 * FHW2, M2 etc are exactly the number you got, so if you got 112, that is what you put in. Standard 90/80/70/60 scale will be used for final grades (curved if necessary). All the exams are open book, open notes. 
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. 23 
History and positioning of AI 
[slides]
History and positioning of AI 
Aug. 25 
Uninformed search

[slides] Uninformed search 
Aug. 30 
Informed search: A* search and heuristics

[slides] Informed search 
Sep. 1 
Constraint satisfaction problems 1

[slides] Constraint satisfaction problems 1 
Sept. 6 
Constraint satisfaction problems 2

[slides] Constraint satisfaction problems 2 Homework 1: Project 1 from the Berkeley AI class. Due September 20th Points are worth as follows: Q1..Q4 25 points each, Q5..A8 10 points each. Total achievable points 140 points. 
Sept. 8 
Game playing and adversarial search

[slides] Adversarial search 
Sept. 13  Expectimax search and utilities

[slides] Expectimax search and utilities 
Sept. 15 
Markov decision processes 1

[slides] Markov Decision Processes 1 
Sept. 20 
Markov decision processes 2

[slides] Markov Decision Processes 2 
Sept. 22 
Reinforcement learning 1

[slides]
Reinforcement learning 1 
Sept. 27 


Sept. 29 

Oct. 4 
Midterm 1: from introduction to Markov Decision Processes (inclusive)  
Oct. 6 
Reinforcement learning 2

[slides]
Reinforcement learning 2 
Oct. 11 

Oct. 13 
Probability

[slides] Probability 
Oct. 18 


Oct. 20 
Markov models

[slides] Markov models 
Oct. 25 
Hidden Markov models

[slides] Hidden
Markov models 
Oct. 27 
Particle filters and applications of HMMs

[slides] Particle filters and Applications of HMMs 
Nov. 1 


Nov. 3 
Classification, principles of machine learning,
naive Bayes

[slides] Classification and naive Bayes Homework 2: Project 4 from the Berkeley AI class. Due November 29th Points are worth as follows: Q1..Q4 25 points each, Q5..A7 30 points each. Total achievable points 190 points. 
Nov. 8 
Neural networks  perceptron

[slides] Perceptron 
Nov. 10 
Midterm exam 2: From reinforcement learning to
particle filters (inclusive) 

Nov. 15 
Casebased reasoning, kernels and clustering

[slides]
Kernels and clustering 
Nov. 17 
Deep learning 
[slides]
Introduction to deep learning 
Nov. 22 
Deep learning 2: Long short term memory 

Nov. 24 
Thanksgiving break  
Nov. 29 
Artificial General Intelligence 1.


Dec. 1 
Artificial General Intelligence 2.


Final exam Thursday, December 08, 2016, 4:00 PM  6:50 PM 