Chen Wang

How LLM's relate to overall machine learning/nn pipeline. How to fine-tune and design LLMs.

Machine Learning, Deep Learning, Transformer Models, Prompting Strategies/Prompt Engineering, RAG, Fine-Tuning, LLM Benchmarking, and Tool-Assisted LLM

Introduction to LLMs, prompt engineering, fine-tuning, RLHF, quantization, pre-training, agents. I think it touched very well on most hot topics. Definitely industry relevant. Instructors gave their best but I think the class was not fully engaged.

如果有志于做generative ai以及llm的话，这门课绝对是堪称私人订制版的合适，甚至我觉得在上完这门课后也可以继续拿着材料继续深挖，毕竟这本身是cs系的课程。但是对于 那些对这个方向没兴趣的学生，这门课可以说是可有可无的水课了。

Definitely a relevant topic in modern statistics, you'll learn a lot if you attend class. But be prepared for very long homeworks that you wont have much time to complete.

Overall, one major issue was the lack of organization with the course. There was no TA until a month into the semester. Additionally, we did not cover everything in the syllabus due to pacing issues. I think another major issue can be responsiveness. Sometimes there would be weeks on end where we would not have any responses on our EdDiscussion forum, leaving quite a bit of confusion. In fairness, the instructors did give us more time to complete assignments when issues like this happened, but it should be better addressed in the upcoming semesters. 1 Columbia University: Arts & Sciences Spring 2025 Course: STATGR5293_003_2025_1-TOPICSINMODERNSTATISTICS : STATGR5293_003_2025_1 - TOPICS IN MODERN STATISTICS Instructor: Chen Wang