CS4804 Fall 2020
This course will introduce the foundations of modern artificial intelligence (AI) and key ideas and techniques underlying the design of intelligent computer systems. It will focus on concepts that are not only important in the space of AI but are also practically useful in modern applications. We will practice effective methods of reasoning about AI problems, which will generalize beyond the specific topics we study in class. Topics include (but are not limited to) search, game playing, logic, machine learning, deep learning, natural language processing, robotics and image processing. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.
Class meets Tuesday and Thursday from 3:30 PM to 4:45 PM using Zoom (Online with Synchronous meetings).
3:00 PM - 4:00 PM
1:00 PM – 3:00 PM
Instructor Office Hours, Zoom
3:30 PM – 4:45 PM
2:00 PM - 3:00 PM
3:30 PM – 4:45 PM
1:30 PM - 3:30 PM
The only official prerequisite is CS 3114 (Undergraduate Data Structures and Algorithms). You should be comfortable with discrete mathematics, basic probability and statistics, basic logic, computational complexity, data structures, and algorithm analysis. The homework assignments will include programming portions using Python.
Please speak with the instructor if you are concerned about your background.
If any student needs special accommodations because of any disabilities, please contact the instructor during the first week of classes. Such students are encouraged to work with The Office of Services for Students with Disabilities to help coordinate accessibity arrangements.
|1||08/24-08/28||Overview of AI (pdf)
Ch.1 & 2
|Uninformed Search (pdf)
|HW0 / P0: Tutorial|
|2||08/31-09/04||Informed Search (pdf) (recording)
|Game, Adversarial Search (pdf) (recording)
|HW1 / P1: Search|
|3||09/07-09/11||Game, Expectimax, Utilities (pdf) (recording)
Ch. 5.5 & 16.1-16.3
|CSP I (pdf) (recording)
|4||09/14-09/18||CSP II (pdf) (recording)
|Propositional Logic (pdf) (recording)
|HW2 / P2: Multi-Agent Search|
|5||09/21-09/25||First-Order Logic (pdf) (recording)
Ch. 8 & 9
|Markov Decision Processes (pdf) (recording)
|6||09/28-10/02||MDP / Reinforcement Learning (pdf) (recording)
Ch. 17-3-17.5 & 22.1
|Reinforcement Learning I (pdf) (recording)
|HW3 / P6: Mini-Contest 1|
|7||10/05-10/09||Reinforcement Learning II (pdf) (recording)
|Probability (pdf) (recording)
|8||10/12-10/16||Bayesian Networks (pdf) (recording)
|10/15 Midterm Exam (Online)
2:30pm – 5:40pm
Exam time 2 hours.
|10/16 Fall Break|
|9||10/19-10/23||Bayesian Networks II (pdf) (recording)
|Decision Networks / HMMs (pdf) (recording)
Ch. 14.1-14.3 & 16.5-16.6
|HW4 / P3: Reinforcement Learning|
|10||10/26-10/30||Particle Filters (pdf) (recording)
|Machine Learning Fundamentals (pdf) (recording)
|HW7 / P7: Mini-Contest 2|
|11||11/02-11/06||Machine Learning II (pdf) (recording)
|Deep Learning Fundamentals (pdf) (recording)
|HW5 / P4: Ghostbusters|
|12||11/09-11/13||Neural Networks & Nature Language Processing (pdf) (recording)
Ch. 21.3-21.6 & Ch 23
|Deep Learning for NLP
Guest: Dr. Lifu Huang (slides) (pdf) (recording)
Guest: Dr. Sahika Genc (AWS)
Guest: Dr. Jia-Bin Huang (recording)
|HW6 / P5: Machine Learning|
|14||11/23-11/27||No class||No class||Thanksgiving Week|
|15||11/30-12/04||The Ethics of AI / Final review (pdf)/(review)
|The Future of AI
(Student Presentation) (recording)
(Mini-Contest Results) (MiniContest1_Top1.pdf)
|HW7 / P7: Mini-Contest 2 Due on 11/30 Monday|
|16||12/07-12/11||No class||No class (Reading day)|
|17||12/14-12/18||No class||No class||12/16 (Wed) Final Exam
(Online)1:05pm – 3:05pm
Exam time 2 hours.
Mini-Contests are programming assignments which covers from homework assignments with more difficult scenarios. There is no single set solution, please bring your own creative ideas!
Requests for regrading due to grading errors must be submitted in writing to a TA within one week of the release of grades.
Homework submitted late without permission will be penalized according to the following formula:
(Penalized score) = (Your raw score) * (1 - 0.1 * (# of days past deadline))
This formula will apply for up to three days, after which the homework will not be accepted and you will receive a grade of zero. Avoid invoking these penalties by starting early and seeking extra help.
Based on the grading breakdown above, each student's final grade for the course will be determined by the final percentage of points earned. The grade ranges are as follows:
The Undergraduate Honor Code pledge that each member of the university community agrees to abide by states: "As a Hokie, I will conduct myself with honor and integrity at all times. I will not lie, cheat, or steal, nor will I accept the actions of those who do."
Students enrolled in this course are responsible for abiding by the Honor Code. A student who has doubts about how the Honor Code applies to any assignment is responsible for obtaining specific guidance from the course instructor before submitting the assignment for evaluation. Ignorance of the rules does not exclude any member of the University community from the requirements and expectations of the Honor Code. For additional information about the Honor Code, please visit: https://www.honorsystem.vt.edu/
This course will have a zero-tolerance philosophy regarding plagiarism or other forms of cheating. Your homework assignments must be your own work, and any external source of code, ideas, or language must be cited to give credit to the original source. I will not hesitate to report incidents of academic dishonesty to the Office of the Undergraduate Honor System.
Because the course will include in-class discussions, we will adhere to Virginia Tech's Principles of Community. The first two principles are most relevant:
The remaining principles are also important and we will take them seriously as a class.