Hands-on with Applied Analytics (IS5126), NUS, Jan-May 2024

• He is a good teacher sharing a lot of knowledge.
• Prof is really passionate about ML, and pushes students to stretch and learn. New students would benefit immensely from his course.
• Good Prof
• Teaching style is detailed and fun. The content is inspiring and in-depth
• Very much involved
• Good teaching , intuitive
• He puts in effort in the content he teaches, responds well to all queries from students, prompts us to think and has genuine care for all his student's learnings.
• He is good at Math
• A balanced teaching style that suits the learning level of the class and also takes into consideration the individual learning ability of the students.
• knowledgeable! Rigorous teaching style!
• Enhanced critical thinking skills.
• can see his passion in teaching; really patient about students' questions; inspire students to overcome challenges and stick on hard problems
• Very knowledgeable and patient to cater for students with not much mathematical or AI background. He would slow down during his mathematical lectures to explain to students who are weaker in Maths. He kindly listens and answer any question the student has, no matter how stupid the question would seem. He really pushes the students to learn to the max and to be well rounded, rather than simply throwing ml library at the problem.
• The professor introduced various types of machine learning algorithms in detail, establishing a framework for us to think and understand the internal logic of the algorithm, which enhanced my thinking and learning ability.
• good at explaining from scratch to understand.
• passionate about teaching and learning, interactive, live class
• He is knowledgeable, patient and cares for his students' learnings.
• Math derivation
• Dumbing down of complex topics with explanations that might help a newbie to get through it as well..
• He can teach boring knowledge in an interesting way. In class, he can well inspire students to think. In addition, his lecture style is very humorous and interesting
• Effective
• Making concepts clear.
• good balance between theories and applications for the course materials. explain the concepts well and really patient about students' questions.
• There is a wide spectrum of students in the class, those with strong AI background and those who are new to the topic. Not sure who is the course for as it seemed at first to be too difficult (requires mathematical and coding pre requisites) for those who are new to AI, but too introductory for those who already specialize in AI (they already learnt more advanced things before the mod). Most of my acquaintances drop out after the first lecture due to the difficulty of the content.
• Would also suggest to leave some time for in class lecture to clarify concept from the videos rather than full lecture on explaining the maths behind the algorithm, so as to strengthen the foundational knowledge of those who are new.
• Videos tend to skip over certain important concepts (eg padding). But otherwise learnt a lot from the videos and Prof's lectures (can't appreciate fully the maths, but did understand most of it)
• There is a bit too much reading to learn in class, and it may not be a good fit for students who have taken five lectures, the workload is a bit heavy.
• Can design the course in such a way to give a flow in learning ML. I felt the topics where here and there and not continuous
• None
• less workload for this course , recording for this course pls
• nil
• It would be nice if teacher could tell us more about the technologies that are being used in industry today.
• Spending more time on explaining the hard concepts and questions, speak slower for sometimes
• More coding in class than theory.
• Maybe go a little bit slower when both writing notes on the board and presenting the thoughs behind it at the same time
• good combination about practical applications and ML theories
• Good learning , interactive class rooms
• Everything, mostly how rounded it is, mathematically, coding, understanding of ml algorithms.
• Hands on
• Teachers are nice in person, trying to be good
• The course reveals the essence of many methods. It is very helpful for learning the entire machine learning framework
• Learn the principles of deep learning models and their practical applications
• practical knowledge taught
• It covers a lot of concepts.
• good combination about practical applications and ML theories
• Good learning , interactive class rooms
• Everything, mostly how rounded it is, mathematically, coding, understanding of ml algorithms.
• Hands on
• Teachers are nice in person, trying to be good
• The course reveals the essence of many methods. It is very helpful for learning the entire machine learning framework
• Learn the principles of deep learning models and their practical applications
• practical knowledge taught
• It covers a lot of concepts.
• None
• Wish the maths had more written explanations. Could possibly provide lecture notes on maths after the lecture?
• felt the topics where not in continuous flow
• for hand derivation, if have electronic version will be great, heavy workload,
• To truly master the details taught by the professor, we need to learn a lot of content by ourselves in advance. Learning new technologies every week will require a heavy workload.
• The workload is a bit heavy
• overwhelming coursework and so many evaluating methods; pace too fast; too many contents and loads for beginners
• Too much irrelevant/relevant stuff, such as the discussion about product lifecycle, which was not discussed in any of the classes and neither asked in any quiz. It was not much useful to go though the videos without relating to it.