CSE 5522 Artificial Intelligence II: Advanced Techniques

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 (10%)

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

Homeworks (50%)

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)
  • Carmen (homework submission)
  • 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. MOSS (Measure of Software Similarity) will be used routinely to detect plagiarism on programming assignments. 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 0 written part and programming part (due 1/12, hand in a paper copy of both parts at the beginning of class)
  • Homework 1 (example - subject to change; due TBA)
  • Homework 2 (example - subject to change; due TBA)
  • Homework 3 (example - subject to change; due TBA)
  • Homework 4 (example - subject to change; due TBA)
  • Anonymous Feedback
    Reading Assignments (subject to change as the spring 2018 term progresses.)
    Date Topic Required Reading Suggested Reading
    1/10 Course Overview Russel & Norvig Chapter 1,2
    1/12 Uninformed Search Algorithms and Their Computational Complexity 3.1-3.4
    1/17 Uninformed Search (cont) and Informed Search 3.6
    1/19 A* Search, Graph Search and Their Completeness and Optimality 3.5, 3.6
    TBD Adversarial Search 5.1,5.2 Google AlphaGo
    TBD Adversarial Search (cont) 5.3,5.4
    TBD Expectimax and Utilities 5.5, 13.1, 13.2, 16.1, 16.2, 16.3
    TBD Utilities (cont) and Markov Decision Processes 17.1, 17.2
    TBD Value Iteration 17.1, 17.2
    TBD Policy Iteration 17.3
    TBD Reinforcement Learning 21.1, 21.2, 21.3
    TBD Reinforcement Learning (cont) 21.1, 21.2, 21.3
    TBD Q-Learning, Function Approximation 21.4, 21.5, 21.6
    TBD Function Approximation (cont), Policy Search, Probability 21.5, 21.6, 13.3, 13.4 Deep Q-Learning
    TBD Probability (cont), Markov Models 13.5, 13.6
    TBD Markov Models (cont) and Hidden Markov Models 15.1, 15.2
    TBD Particle Filtering, DBNs, Speech Recognition 15.3, 15.5
    TBD Bayes Nets 14.1, 14.2
    TBD Bayes Nets (D-Seperation) 14.1, 14.2
    TBD Bayes Nets (Inference) 14.4
    TBD Bayes Nets (Sampling) 14.5
    TBD Machine Learning (Naive Bayes) 20.1, 20.2.1, 20.2.2
    TBD Machine Learning (Perceptron) 18.6
    TBD Final Exam