Deep Learning for Finance (FT5011), NUS, Jan-May 2026

• Best teacher
• He always has clear outline and intuition before diving into complicate principles and mechanisms. Also he's very patient with teaching theoretical deriving and reasoning. Helped me a lot solid my theoretical foundation of Deep Learning.
• One of the best lecturers i have been taught by in NUS so far. His lecturers are a joy to be a part of and i truly learned a lot from his class.
• I believe one can love a subject mostly because of the teacher. Prof Stanley is amazing at how he teaches.
• Very enthusiastic in class and teaches very carefully.
• Very impressive lectures
• One of the best classes I've been to in my entire masters
• He's a good teacher.
• I would like to strongly nominate Prof. Kok for a teaching award. Since high school, I have rarely encountered such careful, detailed, and dedicated teaching. He is extremely passionate about teaching and consistently puts great effort into explaining concepts thoroughly and clearly. His attention to detail and commitment to students' understanding have made a significant difference in my learning experience. Among all the courses in my master's program, this is the course in which I have learned the most. I sincerely appreciate his enthusiasm, patience, and professionalism, and I believe he is highly deserving of this recognition.
• responsible, can guide the student
• Prof. Stanley Kok actively engages students during lectures by asking thoughtful questions and encouraging participation. His use of regular check-ins ensures that students stay consistent with the material and remain actively involved throughout the course.
• Clear outline and intuition before diving into complicate principles and mechanisms. Very patient with teaching theoretical deriving and reasoning.
• Prof Stanley is very technical in nature, his enthusiasm on the subject and his ability to teach the class makes it a joy to be a student in his class. In addition, the way he conveys his lectures and expertise is subject to none
• He explains very complex topics by breaking them down in easy term.
• Very enthusiastic in class and teaches very carefully.
• He's amazing at explaining complex concepts and making them accessible. Also, he's very passionate and funny.
• well prepared, clearly illustrate the thought
• Prof. Stanley Kok's teaching style is outstanding - starts with clear intuition to build understanding, then dives deep into theory and mathematical details. This structured approach makes even complex deep learning topics very accessible. The course is very well structured, and the textbook aligns perfectly with the syllabus. The weekly check-ins are highly effective in ensuring students stay consistent and engaged with the material.
• Maybe at the start of the semester, the prerequisites required to understand this course could be represented in the form of a cheatsheet - more like a mathmatical prerequisite so that it doesnt have to be repeated/can be skimmed passed during the lecture.
• Perhaps the check ins at the start of the class could be less formal and have less weightage in terms of grades, but as actual check ins that consolidates the knowledge of previous topics.
• The course is strong on theory; however, incorporating a short practical demonstration (around 10-15 minutes) to highlight key concepts could further strengthen learning by bridging the gap between theory and real-world application.
• Enabled me to learn numerous advanced technologies.
• I enjoyed everything about the course, its one of the best courses i have taken throughout my 6 year tenure in NUS. My only regret was not being able to take it sooner. Every lesson enhanced my ability and challenged my thinking, i think prof stanley is an excellent prof.
• The lecture content which covers complete spectrum of deep learning topics. It gave me a sense of learning relevant, modern AI topics.
• The deep theoretical foundations as well as a very well structured syllabus and teaching
• How the prof teaches is the highlight of the course.
• I wish GPU's were provided or sources for where we can get gpu's could be shared.
• Nothing dislike. But adding few practical demos could have helped to enhance practical applicability. This was done using assignments. But just a short walk through of model code for transformers, GNN, VAE, diffusion model would be very much added value to students who didn't take ML courses before.
• Some of the math at the end is a little too difficult, i don't know how to manage it if you have gaps in your mathematical understanding
• Didn't enjoy the group project portion though.