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CSE 571: Artificial Intelligence

Fall 2019

03:00 pm - 04:15 pm Tuesdays and Thursdays | CAVC 351


Prof. Siddharth Srivastava

Office Hours    09:00 am - 11:00 am Tuesdays | BYENG 592

Contact Details    siddharths@asu.edu | BYENG 592

Teaching Assistant

Pulkit Verma

TA Office Hours    11:00 am - 12:00 noon Mondays and 10:00 am - 11:00 am Fridays | BYENG 221 (CIDSE TA Lab)

TA Contact Details    verma.pulkit@asu.edu


Abhyudaya Srinet (asrinet1@asu.edu)


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
Project Timeline
1 Thu Aug 22 Introduction: AI agents, models
Homework 0
2 Tue Aug 27 Environment Types 2.1-2.3
3 Thu Aug 29 ROS Tutorial
4 Tue Sep 3 Search: Uninformed 3.1-3.4
Homework 0
5 Thu Sep 5 Search: Informed 3.5-3.6
6 Tue Sep 10 Constraint Satisfaction Problems 6.1-6.5
Homework 1
7 Thu Sep 12 Tutorial: CSP and PDDL Planning 10.1-10.3
8 Tue Sep 17 Representation and Modeling in AI
9 Thu Sep 19 Planning in Deterministic Environments 1 10.1-10.3
10 Tue Sep 24 PDDL Review
Homework 1
11 Thu Sep 26 Stochasticity in Planning 13.1-13.5 Project Teams Announced
12 Tue Oct 1 Markov Decision Processes 1 17.1-17.3
Homework 2
13 Thu Oct 3 Markov Decision Processes 2 17.1-17.3
14 Tue Oct 8 Markov Decision Processes Review 17.1-17.3 Project Proposals Due
15 Thu Oct 10 In-class MID-TERM
16 Thu Oct 17 Reinforcement Learning 1 21.1-21.3
Homework 2
17 Tue Oct 22 Reinforcement Learning 2 21.3-21.6
18 Thu Oct 24 Probabilistic Inference 1 14.1-14.4
19 Tue Oct 29 Probabilistic Inference 2 14.1-14.4
Homework 3
20 Thu Oct 31 Probabilistic Inference 3 14.4-14.5
21 Tue Nov 5 Probabilistic Inference 4 14.4-14.5
22 Thu Nov 7 Tutorial: DBN
23 Tue Nov 12 Partially Observable MDPs (POMDPs) 17.4
Homework 3
24 Thu Nov 14 Game Trees 5.1-5.3 Theory Project Deadline (Nov 13)
25 Tue Nov 19 Statistical Learning 18.1-18.5
26 Thu Nov 21 Neural Networks 18.7
27 Tue Nov 26 Project Presentations
Homework 4
Programming Project Deadline (Nov 25)
28 Tue Dec 3 Project Presentations
29 Thu Dec 5 Project Presentations
Homework 4
30 Tue Dec 10 FINAL EXAM - 02:30 PM - 04:20 PM

Note: The final exam will be scheduled according to ASU's pre-defined examination dates. Please check the University Exam Calendar for more details.

Course format

There will be regular meetings on Tuesdays and Thursdays (unless stated otherwise). Students will be evaluated based on homeworks including programming assignments, midterm and final exams, in-class activity and projects.

Homeworks must be typeset using word processing software (e.g., Word, LaTex or equivalent) and submitted before the due date indicated on the homework. Where applicable, students will also need to submit the source code for programming portions of homeworks. Homeworks submitted up to 48 hours after the due date will receive 30% of the scored credit; homeworks submitted more than 48 hours after the due date will not be graded. Homeworks and exams will be assigned to individuals (not groups); each student is required to submit his or her own original work through canvas.

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


CSE 310
Proficiency with the Python programming language on Ubuntu.


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.]