CAP 5636 - Advanced Artificial Intelligence

Fall 2019

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 the students will be able to: understand the search and decision making techniques used in modern artificial intelligence apply artificial intelligence techniques in their own code understand the societal and ethical implications of artificial intelligence
Instructor:	Dr. Lotzi Bölöni
Office:	HEC - 319
Phone:	(407) 823-2320 (on last resort)
E-mail:	lboloni@ucf.edu (preferred means of communication)
Web Site:	http://www.cs.ucf.edu/~lboloni/Teaching/CAP5636_Fall2019/index.html The assignments and the other announcements will be posted on the course web site
Classroom:	HEC 0119
Class Hours:	Tue, Th 1:30PM - 2:45PM
Office Hours:	Tue, Th 12:00PM - 1:15PM
Pre-requisites:	CAP 4630, or consent of instructor.
Required texts:	There is no required textbook.
Recommended readings:	Stuart Russel and Peter Norvig, Artificial Intelligence - A Modern Approach, 3rd edition
Grading:	Only full grades will be used based on the points obtained. A for 90 and above, B for 80-89, C for 70-79, F for lower than 70. Points awarded: Midterm 1: 20 points, Midterm 2: 20 points, Homeworks/Projects: 30 points total, Final exam 30 points. Some midterms, exams and homeworks will have bonus points, but no curve will be applied. All the exams are open book, open notes. E-books on phones or tablets are acceptable (they must be in airplane mode). Laptops should not be used on exams. Make up exams will be given only in justified cases.
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.
Course accessibility:	The University of Central Florida is committed to providing access and inclusion for all persons with disabilities. Students should connect with Student Accessibility Services (Ferrell Commons 185, sas@ucf.edu, phone (407) 823-2371). Through Student Accessibility Services, a Course Accessibility Letter may be created and sent to professors, which informs faculty of potential access and accommodations that might be reasonable. If you are a deployed active duty military student and feel that you may need a special accommodation due to that unique status, please contact your instructor to discuss your circumstances.
Campus safety:	Emergencies on campus are rare, but if one should arise in our class, everyone needs to work together. Students should be aware of the surroundings and familiar with some basic safety and security concepts. In case of an emergency, dial 911 for assistance. Every UCF classroom contains an emergency procedure guide posted on a wall near the door. Please make a note of the guide's physical location and consider reviewing the online version. If there is a medical emergency during class, we may need to access a first aid kit or AED (Automated External Defibrillator). To learn where those items are located in this building, see the link (click on link from menu on left). To stay informed about emergency situations, sign up to receive UCF text alerts by going to my.ucf.edu and logging in. Click on "Student Self Service" located on the left side of the screen in the tool bar, scroll down to the blue "Personal Information" heading on your Student Center screen, click on "UCF Alert," fill out the information, including your e-mail address, cell phone number, and cell phone provider, click "Apply" to save the changes, and then click "OK."

Syllabus

Date	Topic	Lecture Notes, Readings, Homeworks
Aug. 27	History and positioning of AI	[slides] History and positioning of AI
Aug. 29	Football day
Sep. 3	Hurricane Dorian
Sep. 5	Hurrican Dorian
Sept. 10	Uninformed search Reflex agents Search problems Depth first and breadth first search Uniform cost search	[slides] Uninformed search
Sep. 12	*Informed search: A search and heuristics** Informed search methods Heuristics Greedy search A* search Graph search	[slides] Informed search
Sep. 17	Game playing and adversarial search Types of games Adversarial search, minimax The problem of depth Evaluation functions Alpha Beta pruning	[slides] Adversarial search
Sep. 19	Expectimax search and utilities Expectimax search Refresher about probabilities Utilities and rationality	[slides] Expectimax search and utilities
Sep. 24	Markov decision processes 1 Defining MDPs: policies and utilities Optimal policy, value of state, value of Q-state	[slides] Markov Decision Processes 1 Homework 1: Project 1 from the Berkeley AI class. Due October 8th Points are worth as follows: Q1..Q4 25 points each, Q5..A8 10 points each. Total achievable points 140 points.
Sep. 26	Markov decision processes 2 Policy iteration	[slides] Markov Decision Processes 2
Oct. 1	Midterm 1	Sample Midterm 1
Oct. 3	Reinforcement learning 1 Reinforcement learning as a twist on MDPs	[slides] Reinforcement learning 1
Oct. 8	Model-based and model-free learning Temporal difference learning
Oct. 15	Reinforcement learning 2 Exploration vs. exploitation, regret Generalization across states Policy search	[slides] Reinforcement learning 2
Oct. 17	Probability Random variables Joint and marginal distributions, conditional distribution	[slides] Probability
Oct. 22	Product rule, chain rule, Bayes' rule Inference Independence
Oct. 24	Markov models Markov chains Conditional independence Stationary distributions	[slides] Markov models
Oct. 29	Hidden Markov models Hidden Markov models Example: robot localization	[slides] Hidden Markov models
Oct. 31	Midterm 2	Sample Midterm 2 Homework 2: Project 3 from the Berkeley AI class. Due November 21st Points are worth as follows: Q1..Q4 25 points each, Q5..A8 10 points each. Total achievable points: 140.
Nov. 5	Most likely explanation Speech recognition
Nov. 7	Particle filters and applications of HMMs Particle filters Robot localization with particle filters Dynamic Bayes nets	[slides] Particle filters and Applications of HMMs
Nov. 12	Classification, principles of machine learning, naive Bayes Classification Model-based classification Naive Bayes Spam filter example Generalization and overfitting Parameter estimation	[slides] Classification and naive Bayes
Nov. 12	Classification and machine learning cont'd
Nov. 14
Nov. 19
Nov. 21	Deep Neural Networks - Introduction Machine learning background Loss functions Optimization, stochastic gradient descent Backpropagation	[slides] Deep learning 1 - Introduction
Nov. 26	-Deep learning cont'd	[slides] Deep learning 2 - Convolutional Networks
Nov. 28	Thanksgiving break - no class
Dec. 3
Dec. 10	Final exam Tuesday December 10, 1:00 PM - 3:50 PM	Sample final exam