CS540 Introduction to Artificial Intelligence
CS540, Summer 2026
Department of Computer Sciences
University of Wisconsin-Madison
# Enrollment Note
This class requires attendance and students should be available to participate in online learning at the day/time listed (USA Central Time). The instructor may offer lectures, discussions, quizzes and/or exams only during the scheduled course times. This class section requires students to be available to participate in online learning at the day/time listed (Central Time):
TWRF 1:00 PM - 2:15 PM Jun 15 - Aug 9
# Course Information
Course learning outcomes: Students gain principles of knowledge-based search techniques; automatic deduction, knowledge representation using predicate logic, machine learning, probabilistic reasoning. Students develop applications in tasks such as problem solving, data mining, game playing, natural language understanding, and robotics.
Number of credits associated with the course: 3
How credit hours are met by the course: This class meets for one 150-minute class periods each week over the semester and carries the expectation that students will work on course learning activities (reading, writing, problem sets, studying, etc) for about 3 hours out of classroom for every class period. The syllabus includes more information about meeting times and expectations for student work.
Prerequisite: (COMP SCI 300 or 367) and (MATH 211, 217, 221, or 275) or graduate/professional standing or declared in the Capstone Certificate in Computer Sciences for Professionals.
Time and Location: TWRF 1:00 - 2:15, on Zoom
Textbook: Artificial Intelligence: A Modern Approach (4th edition). Stuart Russell and Peter Norvig. Pearson, 2020. ISBN 978-0134610993. (textbook is optional, but may be useful)
# Course Objectives
📗 Understand and be able to apply the foundational tools in Machine Learning and Artificial Intelligence: Linear algebra, Probability, Logic, and elements of Statistics.
📗 Understand core techniques in Natural Language Processing (NLP), including bag-of-words, tf-idf, n-Gram Models, and Smoothing.
📗 Understand the basics of Machine Learning. Identify and summarize important features in supervised learning and unsupervised learning.
📗 Distinguish between regression and classification, and understand basic algorithms: Linear Regression, k-Nearest Neighbors, and Naive Bayes.
📗 Understand the basics of Neural Networks: Network Architecture, Training, Backpropagation, Stochastic Gradient Descent.
📗 Learn aspects of Deep Learning, including network architectures, convolution, training techniques.
📗 Understand the fundamentals of Game Theory.
📗 Understand how to formulate and solve several types of Search problems.
📗 Understand basic elements of Reinforcement Learning.
📗 Consider how Artificial Intelligence and Machine Learning problems are applied in Real - World settings and the Ethics of Artificial Intelligence.
# Lecture Delivery
In the regular lecture time, we will have online class during which the instructor will lecture, the class will engage in Q&A, quizzes, and discussions.
Each lecture will be a series of short mini-lectures. The lecture will be divided into three blocks. In each block, the instructor will cover some content, and then deliver short quiz questions to clear up any confusion before proceeding to the next block. We would like, whenever possible, all students to participate in the quiz. The students can ask questions anytime during the lecture and can post questions on Piazza after class.
# Piazza
We will use Piazza for Q&A outside lectures. Please follow these rules:
📗 Please check if someone has posted the same or similar question before you; it’s much easier if we build on the thread.
📗 Use an informative Summary line to help others.
In summary: In class you will attend real-time mini-lectures by the instructor, ask and discuss questions, and take short quizzes for student understanding.
# Grading (Subject to Change)
The following weights are used:
📗 Exam: 20 points (2 exams, 10 points each)
📗 In-class Quiz: 20 points (choose 20 lectures out of 28, 1 point each).
📗 Projects: 60 points (choose 6 projects out of 8, 10 points each).
Alternative weights:
📗 Exam: 20 points (2 exams, 10 points each)
📗 Projects: 80 points (all 8 projects, 10 points each).
OR
📗 In-class Quiz: 20 points (choose 20 points out of 28, 1 point each).
📗 Projects: 80 points (all 8 projects, 10 points each).
➩ Each project has a regular component (5 points) and a competition component (5 points). Some competitions are between individual students and some between groups.
➩ The regular components have recommended due dates and can be submitted before two days after the final exam.
➩ The competition components of the projects have strict due dates, and late submissions will not be accepted. The projects will be graded based on competitive ranking in the class: top 20 percent gets 5/5, next 20 percent gets 4/5, ..., bottom 20 percent gets 1/5, no or late submissions get 0/5. Percentages might be adjusted in case more students have high quality submissions.
The option that leads to a higher numerical grade for each individual student will be applied. At the end of the semester, the final letter grades are given based on the following conversion table:
📗 A: 91+ points
📗 AB: 86+ points
📗 B: 81+ points
📗 BC: 76+ points
📗 C: 71+ points
📗 D: 61+ points
As student performance may vary from semester to semester, the instructors reserve the right to adjust this distribution.
# Homework Policies
Projects can be completed using any programming language. Frequently-asked questions (FAQs) on projects will be posted on Piazza.
We encourage you to use a study group for completing your projects. Students are expected to help each other out, and if desired, form ad-hoc groups. However, each student must produce and turn in their own, unique work.
We encourage you to use large language models (LLMs) to help completing your projects and exams. Using code generated by LLMs with proper attribution (i.e. citing the LLM and prompts you used) is not considered an academic offense (for this course), but using LLMs without giving proper attribution is considered an academic offense and will be reported to the
Office of Student Conduct.
# Exams
There will be two optional exams. Makeup exams will not be scheduled. Please plan for exams at these times and let us know about any exam conflicts during the first two weeks of the semester. If an emergency arises that conflicts with the exam times, email us as soon as possible. Emergency exam conflicts will be handled on a case-by-case basis.
McBurney Center students should contact the instructors to specify any special requests for the exams or homework assignments together with the supporting documentation provided by the McBurney Center. We will do our best to accommodate the requests.
Exam grading questions must be raised with the instructor within 72 hours after it is returned. If a regrade request is submitted for a part of a question on the exam, the grader reserves the right to regrade the entire exam and could potentially take points off.
Midterm: TBA
Final: TBA
# Office Hours
Instructors, TAs, peer mentors will hold office hours online. See the office hours page for times and locations.
# Academic Integrity
You are encouraged to discuss with your peers, the TA or the instructors ideas, approaches and techniques broadly. However, all examinations and projects must be written up individually. For example, code for programming assignments must not be developed in groups, nor should code be shared. Make sure you work through all problems yourself, and that your final write-up is your own. If you feel your peer discussions are too deep for comfort, declare it in the homework solution: “I discussed with X,Y,Z the following specific ideas: A, B, C; therefore our solutions may have similarities on D, E, F…”.
You may use books or legit online resources to help solve homework problems, but you must always credit all such sources in your writeup and you must never copy material verbatim.
Cheating and plagiarism will be dealt with in accordance with University procedures (see the
UW-Madison Academic Misconduct Rules and Procedures)
# Disability Information
The University of Wisconsin-Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW-Madison policy (Faculty Document 1071) require that students with disabilities be reasonably accommodated in instruction and campus life. Reasonable accommodations for students with disabilities is a shared faculty and student responsibility. Students are expected to inform the instructors of their need for instructional accommodations by the end of the third week of the semester, or as soon as possible after a disability has been incurred or recognized. The instructors will work either directly with the student or in coordination with the McBurney Center to identify and provide reasonable instructional accommodations. Disability information, including instructional accommodations as part of a student’s educational record, is confidential and protected under FERPA.(See:
McBurney Disability Resource Center)
Last Updated: April 13, 2026 at 1:52 AM