Course Information:

  • IDS 595 - Generative AI
  • Spring 2024
  • Taft Hall | Room 301
  • Tue. 3:30pm - 5:30pm
  • yuhenghu at uic dot edu
  • Office hour: Email


Generative AI models such as GPTs are widely used in many applications in language with significant business implications. This course covers mathematical and computational foundations of generative AI models for language, as well as applications in engineering, design, and science. Specific topics include Seq2Seq, Transformers, and prompt programming. Social issues in generative AI will also be discussed, including topics of justice, safety, and law.

Academic Integrity

You are expected to adhere to the highest standards of academic honesty. Unless otherwise specified, collaboration on assignments is not allowed. Use of published materials is allowed, but the sources should be explicitly stated in your solutions. Violations will be reviewed and sanctioned according to the University Policy on Academic Integrity. Collaborations among team members are only allowed for the final term projects that are selected. "Academic integrity is the pursuit of scholarly activity free from fraud and deception and is an educational objective of this institution. Academic dishonesty includes, but is not limited to, cheating, plagiarizing, fabricating of information or citations, facilitating acts of academic dishonesty by others, having unauthorized possession of examinations, submitting work for another person or work previously used without informing the instructor, or tampering with the academic work of other students." For more information about violations of academic integrity and their consequences, consult

Weekly Schedule

Lecture 1 Intro to LLM and deep learning basics
Lecture 2 Sequence models
Lecture 3 More Sequence models and Attention Mechanism
Lecture 4 Transfomer Models
Lecture 5 BERT and GPT Models
Lecture 6 Data Preparation for LLM
Lecture 7 In-Context Learning
Lecture 8 SFT and RLHF
Lecture 9 LLM Applications: reasoning, planning, agents
Lecture 10 LLM evaluations
Lecture 11 LLM influences and implications
Lecture 12 LLM influences and implications
Lecture 13 LLM influences and implications
Lecture 14 Final Presentation