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CSE 471: Introduction to Artificial Intelligence

Fall 2020

01:30 pm - 02:45 pm Tuesdays and Thursdays


Siddharth Srivastava
Office hours
09:00 am - 10:00 am, Thursdays

Teaching Assistant (TA)

Rushang Karia
Office hours
12:00 pm - 01:00 pm, Fridays


This course provides an introduction 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 (Textbook)
1 Thu Aug 20 Introduction: AI agents, models 1.1, 1.2, 2.1, 2.2
2 Tue Aug 25 Environment Types 2.3, 2.4
3 Thu Aug 27 ROS Tutorial
4 Tue Sep1 Uninformed Search 3.1-3.4
5 Thu Sep 3 Informed Search 3.5, 3.6
6 Tue Sep 8 Constraint Satisfaction Problems 6.1-6.5
7 Thu Sep 10 Representation and Modeling in AI
8 Tue Sep 15 Tutorial: CSP and PDDL Planning 6.1-6.5, 11.1-11.3
9 Thu Sep 17 Planning in Deterministic Environments 11.1-11.3
10 Tue Sep 22 Planning in Stochastic Environments 12.1-12.5
11 Thu Sep 24 Markov Decision Processes 1 17.1-17.3
12 Tue Sep 29 Tutorial: PDDL Review
13 Thu Oct 1 Markov Decision Processes 2 17.1-17.3
14 Tue Oct 6 Markov Decision Processes 3 17.1-17.3
15 Thu Oct 8 Tutorial: Markov Decision Processes 17.1-17.3
16 Tue Oct 13 MID-TERM EXAM
17 Thu Oct 15 Reinforcement Learning 1 22.1-22.3
18 Tue Oct 20 Reinforcement Learning 2 22.3-22.6
19 Thu Oct 22 Probabilistic Inference 1 13.1-13.4
20 Tue Oct 27 Probabilistic Inference 2 13.1-13.4
21 Thu Oct 29 Probabilistic Inference 3 13.1-13.4
22 Tue Nov 3 Tutorial: Probabilistic Inference
23 Thu Nov 5 Partially Observable MDPs (POMDPs) 17.4-17.5
24 Tue Nov 10 Game Trees 5.1-5.3
25 Thu Nov 12 Statistical Learning 19.1-19.5
26 Tue Nov 17 Neural Networks 1 21.1-21.4
27 Thu Nov 19 Neural Networks 2 21.4-21.7
28 Tue Nov 24 Applications, Review and Discussion
29 Tue Dec 1 FINAL EXAM

Course format

There will be regular meetings on Tuesdays and Thursdays (unless stated otherwise). Students will be evaluated based on class participation (2%); and a best of two of Homework: 50 points, in-class quizzes: 50 points and exams: 50 points. Extra credit may be awarded for those who do well on all three.

Students who are facing difficulties this semester have the option of receiving a "Y" grade in this course. The minimum requirement is qualifying for a "C" grade. Students who wish to use this option to get a "Y" in lieu of a standard letter grade should send an email to the class email id (asu.cse471@gmail.com) with the subject "471: Y grade option" and indicate the grade ranges over which they would prefer a Y. This mail should be sent before November 15th and it will be considered binding.

Where applicable, students will also need to submit the source code for programming assignments. Assignments submitted up to 48 hours after the due date will receive 30% of the scored credit; assignments submitted more than 48 hours after the due date will not be graded. All quizzes, assignments and exams are assigned to individuals (not groups); each student is required to submit his or her own original work through canvas.

ASU Sync

This course uses Sync. ASU Sync is a technology-enhanced approach, designed to meet the dynamic needs of the class. During Sync classes, students learn remotely through live class lectures, discussions, study groups and/or tutoring. You can find out more information about ASU Sync for students at https://provost.asu.edu/sync/students and https://www.asu.edu/about/fall-2020.

Lectures will feature extensive instructor-student interaction and will be delivered live online to registered students. Details about attending the lectures will be announced to registered students on Canvas.


  • CSE 310
  • Proficiency with the Python programming language on Linux systems, e.g. Ubuntu
  • Basics of probability theory


Textbook: Artificial Intelligence: A Modern Approach, 4th Edition by Stuart Russell and Peter Norvig
Online Discussions and Polls: Slack Workspace

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. In ASU Sync classes, please keep your mic muted unless instructed otherwise by the instructor.

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.

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 the 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.)