CS540 Introduction to Artificial Intelligence


CS540, Fall 2026

Department of Computer Sciences

University of Wisconsin-Madison


# Epic Section Enrollment Note


📗 This course website is only for the Epic section of CS540, Fall 2026. UW Madison students should refer to the Campus section website.

# 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: W5:30-8:30, Epic Campus
 
 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:

📗 In-class Quiz: 40 points (choose 10 lectures out of 14, 4 points each).
📗 Projects: 60 points (choose 6 projects out of 8, 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 last lecture.
➩ 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.

At the end of the semester, the final letter grades are given based on the following conversion table:

📗 A: 80+ points
📗 AB: 75+ points
📗 B: 70+ points
📗 BC: 65+ points
📗 C: 60+ points
📗 D: 55+ 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. 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).

# 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 19, 2026 at 11:47 AM