CSE 571: Artificial Intelligence

Spring 2019

04:30 pm - 05:45 pm Tuesdays and Thursdays | CAVC 351


Instructor

Prof. Siddharth Srivastava

Office Hours    09:00 am - 10:00 am Tuesdays and Thursdays | BYENG 592

Contact Details    siddharths@asu.edu | BYENG 592


Teaching Assistant

Naman Shah

TA Office Hours    04:00 pm - 05:00 pm Wednesdays and Fridays | BYENG 221 (CIDSE TA Lab)

TA Contact Details    npshah4@asu.edu


Graders

Chirav Dave (cdave1@asu.edu)

Ketan Patil (kkpatil1@asu.edu)


Overview

This course introduces you to Artificial Intelligence and covers basic ideas and methods in AI. Students will learn about informed and uninformed search, probabilistic inference, Markov decision processes, reinforcement learning, statistical learning and modeling and representation of problems.


Tentative Schedule

Lecture Number Date Topic(s) Prior Reading
Assignments
Project Timeline
(Textbook)
Released
Due
1 Tue Jan 8 Introduction: AI agents, models
Homework 0
2 Thu Jan 10 Environment Types 2.1-2.3
3 Tue Jan 15 Search: Uninformed 3.1-3.4
4 Thu Jan 17 Search: Informed 3.5-3.6
Homework 0
5 Tue Jan 22 Constraint Satisfaction Problems 6.1-6.5
Homework 1
6 Thu Jan 24 Representation and Modeling in AI
7 Tue Jan 29 Tutorial: PDDL Planning 10.1-10.3
8 Tue Feb 5 Planning in Deterministic Environments 1 10.1-10.3
Homework 1
9 Thu Feb 7 Planning in Deterministic Environments 2 10.1-10.3
10 Tue Feb 12 Partial Observability 13.1-13.5
Homework 2
Project Teams due
11 Thu Feb 14 Probabilistic Inference 1 14.1-14.4
12 Tue Feb 19 Probabilistic Inference 2 14.4-14.5
13 Thu Feb 21 Markov Decision Processes 1 17.1-17.3
14 Tue Feb 26 Take-home MID-TERM
Midterm
Project Proposals due
15 Thu Feb 28 MID-TERM due
Midterm
16 Tue Mar 12 Markov Decision Processes 2 17.1-17.3
17 Thu Mar 14 Markov Decision Processes 3 17.1-17.3
Homework 2
18 Tue Mar 19 Reinforcement Learning 1 21.1-21.3
19 Thu Mar 21 Reinforcement Learning 2 21.3-21.6
Homework 3
20 Tue Mar 26 Partially Observable MDPs (POMDPs) 17.4
21 Thu Mar 28 Tutorial: POMDP solvers 17.4
22 Tue Apr 2 Game Trees 5.1-5.3
23 Thu Apr 4 Statistical Learning 18.1-18.5
Homework 3
24 Tue Apr 9 Neural Networks 18.7
Homework 4
25 Thu Apr 11 Robotics 1 25.1-25.2
26 Tue Apr 16 Robotics 2 25.4.1
Projects due
27 Thu Apr 18 Project Presentations
Homework 4
28 Tue Apr 23 Project Presentations
29 Thu Apr 25 In-class FINAL (04:30 pm - 06:30 pm)

Course format

There will be regular meetings on Tuesdays and Thursdays (unless stated otherwise) covering fundamentals of AI. Programming assignments will be released bi-weekly. Homeworks will be assigned bi-weekly. Homeworks must be typeset using word processing software (E.g. Word or equivalent) and submitted before the due date indicated on the homework.
There will be no groups for programming assignments and homeworks. Each student is required to submit their own work through canvas. Do not try to gain credit for someone else’s work.

Note: Do not submit a scanned copy of the homework. Acceptable formats are: doc, docx or pdf.

Prerequisites

CSE 310
Proficiency with the Python programming language.

Resources

Textbook   Artificial Intelligence: A Modern Approach, 3rd Edition by Stuart Russell and Peter Norvig
Online Discussion and Polls: Canvas course page.

Absence and Make-up Policies

If a student is unable to take up the mid-term or final exam due to unavoidable circumstances, it is the student’s responsibility to notify the instructor beforehand to make necessary accommodations, if possible. Students are advised to notify the instructor as early as possible. Students who expect to miss class due to officially university-sanctioned activities should inform the instructor early in the semester. Alternative arrangements will generally be made for any examinations and other graded in-class work affected by such absences. Absences not noted at the beginning of class will be considered only with a note from a reliable third-party (e.g., a doctor).

The preceding policies are based on ACD 304–04, “Accommodation for Religious Practices” and ACD 304–02, “Missed Classes Due to University-Sanctioned Activities.”


Classroom Behavior

Cell phones and pagers must be turned off during class to avoid causing distractions, unless instructed by the presenters (e.g., for online polling software). The use of recording devices is not permitted during class. Any violent or threatening conduct by an ASU student in this class will be reported to the ASU Police Department and the Office of the Dean of Students.


Academic Integrity

All students in this class are subject to ASU’s Academic Integrity Policy (available at http://provost.asu.edu/academicintegrity) and should acquaint themselves with its content and requirements, including a strict prohibition against plagiarism.

  • On first detection of cheating, student(s) shall get:
    • Zero points for that quiz/assignment/exam
    • Grade scales down; max becomes C for all students involved
    • If you would have received a C without that quiz/assignment/exam, now you get a D
    • To get a C you need to score at the level of A or above in the rest of the course
  • Second detection:
    • Report to Dean’s office
    • Grade X

    All violations will be reported to the Dean’s office, who maintain records of all offenses. Students are expected to abide by the FSE Honor Code (http://engineering.asu.edu/integrity/)


    Disability Accommodations

    Suitable accommodations will be made for students having disabilities and students should notify the instructor as early as possible if they will require same. Such students must be registered with the Disability Resource Center and provide documentation to that effect.


    Sexual Discrimination

    Title IX is a federal law that provides that no person be excluded on the basis of sex from participation in, be denied benefits of, or be subjected to discrimination under any education program or activity. Both Title IX and university policy make clear that sexual violence and harassment based on sex is prohibited. An individual who believes they have been subjected to sexual violence or harassed on the basis of sex can seek support, including counseling and academic support, from the university. If you or someone you know has been harassed on the basis of sex or sexually assaulted, you can find information and resources at https://sexualviolenceprevention.asu.edu/faqs.
    As a mandated reporter, I am obligated to report any information I become aware of regarding alleged acts of sexual discrimination, including sexual violence and dating violence. ASU Counseling Services, https://eoss.asu.edu/counseling, is available if you wish discuss any concerns confidentially and privately.


    Notice: Any information in this syllabus (other than grading and absence policies) may be subject to change with reasonable advance notice.

    Notice: All contents of these lectures, including written materials distributed to the class, are under copyright protection. Notes based on these materials may not be sold or commercialized without the express permission of the instructor. [Note: Based on ACD 304-06.]