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:
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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: |
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Grading: |
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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.
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Date |
Topic |
Lecture Notes, Readings, Homeworks |
Aug. 27 |
History and positioning of AI |
[slides]
History and positioning of AI |
Aug. 29 |
Football day |
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Sep. 3 |
Hurricane Dorian |
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Sep. 5 |
Hurrican Dorian |
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Sept. 10 |
Uninformed search
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[slides] Uninformed search |
Sep. 12 |
Informed search: A* search and heuristics
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[slides] Informed search |
Sep. 17 |
Game playing and adversarial search
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[slides] Adversarial search |
Sep. 19 | Expectimax search and utilities
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[slides] Expectimax search and utilities |
Sep. 24 |
Markov decision processes 1
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[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
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[slides] Markov Decision Processes 2 |
Oct. 1 |
Midterm 1 |
Sample Midterm 1 |
Oct. 3 |
Reinforcement learning 1
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[slides]
Reinforcement learning 1 |
Oct. 8 |
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Oct. 15 |
Reinforcement learning 2
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[slides]
Reinforcement learning 2 |
Oct. 17 |
Probability
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[slides] Probability |
Oct. 22 |
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Oct. 24 |
Markov models
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[slides] Markov models |
Oct. 29 |
Hidden Markov models
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[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 |
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Nov. 7 |
Particle filters and applications of HMMs
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[slides] Particle filters and Applications of HMMs |
Nov. 12 |
Classification, principles of machine learning,
naive Bayes
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[slides] Classification and naive Bayes |
Nov. 12 |
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Nov. 14 |
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Nov. 19 |
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Nov. 21 |
Deep Neural Networks - Introduction
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[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 |
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Dec. 10 |
Final exam Tuesday December 10, 1:00 PM - 3:50 PM |
Sample final exam |