CAP 5636 - Advanced Artificial Intelligence

Fall 2021

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:	Ladislau.Boloni@ucf.edu (preferred means of communication)
TA:	Qibing Jiang qibingjiang@knights.ucf.edu
Web Site:	http://www.cs.ucf.edu/~lboloni/Teaching/CAP5636_Fall2021/index.html The assignments and the other announcements will be posted on the course web site
Classroom:	HEC 103
Class Hours:	Tue, Th 12:00PM - 1:15PM
Office Hours:	Tue, Th 6:00PM - 7:30PM See webcourses announcement for Zoom link.
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, 4rd 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. The exams will be administered through ProctorHub, and are open book, open notes. Make up exams will be given only in justified cases.
Sample exams	Sample Midterm 1 Sample Midterm 2 Sample Final Exam Note: you should not expect that the new exams are just variations with different data.
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
Tue, Aug. 24	History and positioning of AI Motivating AI. Dangers of AI and AGI. Early history Expert systems	[slides] History and positioning of AI Homework 1 - Introduce yourself - due Aug 30, 2021
Thu, Aug. 26	History and positioning of AI Neural networks The two intellectual traditions of logic vs neural networks A melting pot of other ideas The agent view of AI
Tue, Aug. 31	Uninformed search Reflex agents Search problems Depth first and breadth first search Uniform cost search	[slides] Uninformed search Homework 2 - Search - due Sept 21, 2021
Thu, Sep. 2
Tue, Sept. 7
Thu, Sep. 9	*Informed search: A search and heuristics** Informed search methods Heuristics Greedy search A* search Graph search	[slides] Informed search
Tue, Sep. 14	Game playing and adversarial search Types of games Adversarial search, minimax The problem of depth Evaluation functions Alpha Beta pruning	[slides] Adversarial search
Thu, Sep. 16	Expectimax search and utilities Expectimax search Refresher about probabilities Utilities and rationality	[slides] Expectimax search and utilities
Tue, Sep. 21	Markov decision processes 1 Defining MDPs: policies and utilities Optimal policy, value of state, value of Q-state	[slides] Markov Decision Processes 1
Thu, Sept. 23	Midterm 1 - Introduction to Expectimax
Tue, Sep. 28	Markov decision processes 2 Policy iteration	[slides] Markov Decision Processes 2
Thu, Sept. 30	Reinforcement learning 1 Reinforcement learning as a twist on MDPs	[slides] Reinforcement learning 1 Homework 3 - Reinforcement learning - due October 28, 2021
Tue, Oct. 5	Model-based and model-free learning Temporal difference learning
Thu, Oct. 7	Reinforcement learning 2 Exploration vs. exploitation, regret Generalization across states Policy search	[slides] Reinforcement learning 2
Tue, Oct. 12	Probability Random variables Joint and marginal distributions, conditional distribution	[slides] Probability
Thu, Oct. 14	Product rule, chain rule, Bayes' rule Inference Independence
Tue, Oct. 19	Markov models Markov chains Conditional independence Stationary distributions	[slides] Markov models
Thu, Oct. 21	Hidden Markov models Hidden Markov models Example: robot localization	[slides] Hidden Markov models
Tue, Oct. 26	Most likely explanation Speech recognition
Th, Oct. 28
Tue, Nov. 2	Midterm 2 - from MDP to Markov Chains
Thu, Nov. 4	Particle filters and applications of HMMs Particle filters Robot localization with particle filters Dynamic Bayes nets	[slides] Particle filters and Applications of HMMs
Tue, Nov. 9	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
Thu, Nov. 11	Veterans Day - no class
Tue, Nov. 16	Classification and machine learning cont'd
Thu, Nov. 18	Neural networks Perceptron	[slides] Perceptrons
Tue, Nov. 23	Machine learning background of deep learning History and impact Machine learning background Loss functions: squared, cross-entropy, softmax Optimization, stochastic gradient descent Backpropagation	[slides] Neural Networks I Homework 4 - due November 30 CAP5636-CatDogMonkey-HW.zip
Thu, Nov 25	Thanksgiving break - no class
Tue, Nov. 30	Feedforward neural networks Feedforward networks Stochastic gradient descent
Thu, Dec. 2	Convolutional neural networks Convolutions Convolutional filters in neural networks Pooling layers	[slides] Convolutional networks
Thu, Dec. 9	Final exam Thursday December 9, 2021 10:00 AM - 12:50 PM