Main class: Time: Mon/Wed 3:30 -- 4:25 pm • Location: LWSN B134
Lab: Time: Friday 9:30 -- 11:20 am • Location: LWSN B160
Course Outline • Edstem • Brightspace • Gradescope • VocareumProfessor Yexiang Xue
Lawson 2142L • yexiang [at] purdue DOT edu •
Office hours: Wednesday before class: 2:30 -- 3:30 pm.
Office hours will only be held if appointments are made via emails at least 24 hours in prior.
Nan Jiang • jiang631 [at] purdue DOT edu • Office hours: 11:30 am -- 12:30 pm every Friday, at the lab classroom.
Questions: We will use Edstem for class questions/discussion. The link to join our class on Edstem will be made available in Brightspace. Instead of sending email to the TA list, please post your questions on Edstem.
This course provides an introduction to foundational areas of artificial intelligence and current techniques for building intelligent systems. As an entry-level class for Artificial Intelligence, the primary goals of this course are:
Math 161/165. CS 180, 182.
No required textbooks. The following optional book is encouraged if you would like to study AI in the future.
More useful books:
A few useful online resources:
Machine learning materials:
More resources coming up.
The class consists of lectures on Mondays and Wednesdays, and laboratories on Fridays.
The labs will be released each week and are expected to be completed at the lab sessions. Most labs use Vocareum.
There will be four homework/programming assignments that will be posted on the schedule. Assignments should be submitted online via gradescope for written questions or via Vocareum for programming assignments. In general, questions about the details of homeworks should be directed to the TA.
There will be an evening midterm and a comprehensive final exam. Exams will be closed book and closed notes.
Assignments are to be submitted by the due date listed. Each person will be allowed three days of extensions which can be applied to any combination of assignments (homework/projects/essays only) during the semester without penalty. After that a late penalty of 15% per day will be assigned. Use of a partial day will be counted as a full day. Use of extension days must be stated explicitly at the time of the late submission (by accompanying email to the TA), otherwise late penalties will apply. Extensions cannot be used after the final day of classes (ie., April 30). Extension days cannot be rearranged after they are applied to a submission. Use them wisely!
Assignments will NOT BE accepted if they are more than five days late. Additional extensions will be granted only due to serious and documented medical or family emergencies.
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 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. A hyperlink to Purdue’s full Nondiscrimination Policy Statement is included here.
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 can discuss options. You are also encouraged to contact the Disability Resource Center at: drc@purdue.edu or by phone: 765-494-1247.
If you find yourself beginning to feel some stress, anxiety and/or feeling slightly overwhelmed, try WellTrack. Sign in and find information and tools at your fingertips, available to you at any time.
If you need support and information about options and resources, please contact or see the Office of the Dean of Students. Call 765-494-1747. Hours of operation are M-F, 8 am- 5 pm.
If you find yourself struggling to find a healthy balance between academics, social life, stress, etc. sign up for free one-on-one virtual or in-person sessions with a Purdue Wellness Coach at RecWell. Student coaches can help you navigate through barriers and challenges toward your goals throughout the semester. Sign up is completely free and can be done on BoilerConnect. If you have any questions, please contact Purdue Wellness at evans240@purdue.edu.
If you’re struggling and need mental health services: Purdue University is committed to advancing the mental health and well-being of its students. If you or someone you know is feeling overwhelmed, depressed, and/or in need of mental health support, services are available. For help, such individuals should contact Counseling and Psychological Services (CAPS) at 765-494-6995 during and after hours, on weekends and holidays, or by going to the CAPS office on the second floor of the Purdue University Student Health Center (PUSH) during business hours.
In the event of a major campus emergency, course requirements, deadlines and grading percentages are subject to changes that may be necessitated by a revised semester calendar or other circumstances beyond the instructor’s control. Relevant changes to this course will be posted onto the course website or can be obtained by contacting the instructors or TAs via email or phone. You are expected to read your @purdue.edu email on a frequent basis.
Introduction (1 week)
What is artificial intelligence? Overview of AI history and motivating application areas.
Learning Python (2 weeks)
Programming using Python.
Data parsing, wrangling, cleaning (1 week)
Using python for data processing.
Knowledge representation (2 weeks)
How to represent knowledge in an AI system.
Learning (2 weeks)
Build a first-hand machine learning system.
Validation, diagnosis, and visualization (2 weeks)
How to make sure the learned knowledge make sense?
Reasoning and decision-making (3 weeks)
How to solve reasoning problems and make complex decisions using AI.
State-of-the-art outlook (2 weeks)
Game playing, causality, fairness/bias/ethics, uncertainty handling, reinforcement learning.