CS 398

Spring 2026 Part of Term 1

Part of Term 1
Jan 20-May 6

Special Topics

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Credit: 1 TO 4 hours.

Subject offerings of new and developing areas of knowledge in computer science intended to augment the existing curriculum. See Class Schedule or departmental course information for topics and prerequisites.

May be repeated in the same or separate terms if topics vary.

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CS 398 class schedule data for spring 2026
	CRN	Type	Section	Time	Day	Location	Instructor	Section Details
	68114	Lecture-Discussion	ALL	11:00AM -12:20PM	TR	3217 Everitt Laboratory	Kozlowski, T	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 3 hours Section Title: Applied Large Language Models Section Info: Applied Large Language Models: Building Custom AI Tools and Applications. Description: The course introduces students to the conceptual foundations and practical uses of transformer-based language models, their structure, capabilities, and limitations. Students will learn to interact with both local open-weight models and remote API-based systems. Through a sequence of hands-on projects, students design and build custom AI tools and applications such as personal interactive chatbot and recommendation system, Q&A document assistants ("talk to my documents"), LLM-based debate agents, and personal tutoring or research agents. Emphasis is placed on experimentation, system design, validation, and responsible use of LLMs. The course is for students with basic programming experience. Prerequisite: CS 101, CS 105, CS 107, or CS 124. Restriction(s): Not intended for Nuclear, Plasma, Radiolgc Engr major(s). Restricted to Undergrad - Urbana-Champaign. Restricted to CS and blended CS majors students.
	61030	Lecture-Discussion	ALM	11:00AM -12:20PM	TR	3217 Everitt Laboratory	Kozlowski, T	Part of Term: 1 Date Range: 01/20/26-05/06/26 Credit: 3 hours Section Title: Applied Large Language Models Section Info: Applied Large Language Models: Building Custom AI Tools and Applications. Description: The course introduces students to the conceptual foundations and practical uses of transformer-based language models, their structure, capabilities, and limitations. Students will learn to interact with both local open-weight models and remote API-based systems. Through a sequence of hands-on projects, students design and build custom AI tools and applications such as personal interactive chatbot and recommendation system, Q&A document assistants ("talk to my documents"), LLM-based debate agents, and personal tutoring or research agents. Emphasis is placed on experimentation, system design, validation, and responsible use of LLMs. The course is for students with basic programming experience. Prerequisite: CS 101, CS 105, CS 107, or CS 124. Restriction(s): Not intended for Nuclear, Plasma, Radiolgc Engr major(s). Restricted to Undergrad - Urbana-Champaign. Not intended for CS and blended CS majors students.