CS 57100: Artificial Intelligence

MWF 1:30-2:20pm

Stanley Coulter Hall (SC) 239

Instructor: Brian Bullins (Office: LWSN 2116N)

Email: bbullins@purdue.edu

Course Outline

Course Topics

Artificial Intelligence (AI) systems are increasingly being deployed in many real-world tasks. This course provides an introduction to the fundamental principles and applications of AI. The course covers classic material including search-based methods, probabilistic reasoning, game playing, decision making, exact and approximate inference, causal learning, and reinforcement learning as well as selected advanced topics. The focus of the course is on foundational methods and current techniques for building AI systems that exhibit intelligent behavior and can learn from experience.

Note that there will be little coverage of machine learning in this course; those whose primary interest is the machine learning aspects of AI should take CS57300 or CS57800.

Prerequisites

The course assumes you are familiar with basic concepts in analysis, linear algebra, optimization (e.g., Math 26500 or 35100); elementary probability, statistics (e.g., Stat 35000 or 35500); and discrete mathematics, data structures, and algorithms (e.g., CS38100). Students are expected to have good programming and software development skills and have a working knowledge of Python and Java.

Teaching Assistants

• Site Bai
  Office hours: Mon. 11:30am-1:20pm, HAAS 143
  Email: bai123@purdue.edu
• Shyamvanshikumar Singh
  Office hours: Tues. and Thurs. 3:15-4:15pm, HAAS 143
  Email: sing1012@purdue.edu

Instructor Office Hours

Available by appointment. (Please email me a time that works for you.)

Mailing List

There will be a course email list (via Brightspace) used for high-priority announcements. This will use your @purdue.edu email address, so please be sure to check this on a regular basis.

We will be using Gradescope to turn in and comment on assignments, and Brightspace for recording and distributing grades, and any other non-public information about the course.

Course Methodology

The course will primarily be taught through lectures, supplemented with reading.

The primary reading will be from the textbook. The written assignments and projects are also a significant component of the learning experience.

We will be using Ed Discussion to facilitate discussions; this will enable you to post questions as well as respond to questions posted by others. If you have not already joined Ed Discussion for this course, the link to do so can be found in the syllabus on Brightspace.

Evaluation/Grading

• Midterm Exam: 20%
• Final Exam: 30%
• Written assignments and programming projects: 25%
• Self-directed final team project: 20%
• Class participation: 5%

Late work will be penalized 15% per day (24 hour period or fraction thereof). The penalty is based on possible points, not your actual score. (E.g., after 5 days, if your submission achieves less than 75% of the possible points, you get a 0.) Each assignment has a hard deadline of five days after the published due date, after which the solution sets go out and no further submissions are accepted. Also note that the last day of class will be a hard deadline for all work. You are allowed five extension days, to be used at your discretion throughout the semester (illness, job interviews, etc.); no penalty is assessed for late work within this limit. If your assignments add up to more than five days late over the semester, the late days will be automatically applied to the highest value assignments (e.g., projects), so the 15% late penalties are applied to the lower value assignments first. Late penalties will only be applied at the end of the semester, so if you go beyond the alotted late days, you may see your score for some assignments drop. You must keep track of late days yourself. (Note: Fractional use is not allowed, and extension days may not be used to extend submission past the hard deadline (i.e., five days after the deadline, or the last day of class, whichever comes first).

Policy on Intellectual Honesty

Please read the departmental academic integrity policy. You should also be familiar with the Purdue University Code of Honor and the Academic Integrity Guide for Students.

Interaction among students is encouraged, and you should feel free to discuss the course with one another. However, unless otherwise noted, the work that you turn in should reflect your own efforts and knowledge.

Other Issues and Resources

University Emergency Preparedness instructions. Note: In the event of weather-related class cancellation, please be sure to check your email.

Nondiscrimination Statement: Purdue University is committed to maintaining a community which recognizes and values the inherent worth and dignity of every person; fosters tolerance, sensitivity, understanding, and mutual respect among its members; and encourages each individual to strive to reach his or her own potential. In pursuit of its goal of academic excellence, the University seeks to develop and nurture diversity. The University believes that diversity among its many members strengthens the institution, stimulates creativity, promotes the exchange of ideas, and enriches campus life. Purdue’s nondiscrimination policy can be found at http://www.purdue.edu/purdue/ea_eou_statement.html.

Purdue University strives to make learning experiences as accessible as possible. If you anticipate or experience physical or academic barriers based on disability, you are welcome to let me know so that we may discuss options. You are also encouraged to contact the Disability Resource Center at: drc@purdue.edu or by phone: (765) 494-1247.

Student Mental Health and Wellbeing: Purdue University is committed to advancing the mental health and wellbeing of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of support, services are available. For help, such individuals should contact Counseling and Psychological Services (CAPS) at (765) 494-6995 and http://www.purdue.edu/caps/ during and after hours, on weekends and holidays, or through its counselors physically located in the Purdue University Student Health Center (PUSH) and the Psychology building (PSYC) during business hours.

Course Textbook

The textbook for this course is:

Artificial Intelligence: A Modern Approach
Stuart Russell and Peter Norvig
Pearson, 2021.
ISBN: 9780137505135

Course Outline (Tentative, Subject to Change):

FINAL EXAM: Tuesday, December 12, 7:00pm-9:00pm ET, MSEE B012