CSE 5522: Survey of Artificial Intelligence II: Advanced Techniques

Survey of advanced concepts, techniques, and applications of artificial intelligence, including knowledge representation, learning, natural language understanding, and vision.

Details
Textbook:
Grading

Grading will be based on:

Participation (5%)

You will receive credit for asking and answering questions related to the homework on Piazza and engaging in class discussion.

Homeworks (55%)

The homeworks will include both written and programming assignments. Homework should be submitted to the Dropbox folder in Carmen by 11:59pm on the day it is due (unless otherwise instructed). Each student will have 3 flexible days to turn in late homework throughout the semester. As an example, you could turn in the first homework 2 days late and the second homework 1 day late without any penalty. After that you will loose 20% for each day the homework is late. Please email your homework to the instructor in case there are any technical issues with submission.

Midterm (20%)

Final Exam (20%)

Resources
  • Piazza (discussion, announcements and restricted resources). https://piazza.com/osu/fall2017/5522/home
  • Carmen (homework submission). https://osu.instructure.com/courses/27789/assignments/414465
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    Academic Integrity
    Any assignment or exam that you hand in must be your own work (with the exception of group projects). However, talking with others to better understand the material is strongly encouraged. Copying a solution or letting someone copy your solution is cheating. Everything you hand in must be your own words. Code you hand in must be written by you, with the exception of any code provided as part of the assignment. Any collaboration during an exam is considered cheating. Any student who is caught cheating will be reported to the Committee on Academic Misconduct. Please don't take a chance - if you are having trouble understanding the material, let us know and we will be happy to help.
    Homework Assignments
  • Homework 1 (written part) (programming part) (Due 8/29, hand in a paper copy of both parts at the beginning of class)
  • Homework 2 (Due 9/14 9/19, follow instructions for submission at the bottom of the assignment)
  • Homework 3 (Due 10/10, follow instructions for submission at the bottom of the assignment)
  • Homework 4 (Due 11/7, follow instructions for submission at the bottom of the assignment)
  • Homework 5 (Due 12/7, follow instructions for submission at the bottom of the assignment)
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    Anonymous Feedback
    https://goo.gl/forms/ajvb9dJaTHsWh7sS2
    Tentative Schedule:
    https://docs.google.com/spreadsheets/d/1-qGfg5wWg9MAYCmja6TlItChcTzusg33_yn_OER7MF4/edit?usp=sharing
    Reading Assignments
    Date Topic Required Reading Suggested Reading
    8/22 Course Overview Russel & Norvig Chapter 1,2
    8/24 Search 3.1-3.4
    8/29 Search (cont) + Informed Search 3.5, 3.6
    8/31 Informed Search (cont) 3.6
    8/31 Adversarial Search 5.1,5.2 Google AlphaGo
    9/13 Adversarial Search (cont) 5.3,5.4
    9/19 Guest Lecture (Wei Xu)
    9/21 Expectimax and Utilities 5.5, 13.1, 13.2, 16.1, 16.2, 16.3
    9/26 Utilities (cont) and Markov Decision Processes 17.1, 17.2
    9/28 Value Iteration 17.1, 17.2
    10/3 Policy Iteration 17.3
    10/5 Reinforcement Learning 21.1, 21.2, 21.3
    10/17 Reinforcement Learning (cont) 21.1, 21.2, 21.3
    10/19 Q-Learning, Function Approximation 21.4, 21.5, 21.6
    10/24 Function Approximation (cont), Policy Search, Probability 21.5, 21.6, 13.3, 13.4 Deep Q-Learning
    10/26 Probability (cont), Markov Models 13.5, 13.6
    10/31 Markov Models (cont) and Hidden Markov Models 15.1, 15.2
    11/7 Particle Filtering, DBNs, Speech Recognition 15.3, 15.5
    11/9 Bayes Nets 14.1, 14.2
    11/14 Bayes Nets (D-Seperation) 14.1, 14.2
    11/16 Bayes Nets (Inference) 14.4
    Bayes Nets (Sampling) 14.5
    Machine Learning (Naive Bayes) 20.1, 20.2.1, 20.2.2
    Machine Learning (Perceptron) 18.6
    12/11 @ 12pm Final Exam