Student Comments for Machine Learning (50.007), SUTD, Sep-Dec 2014

• The instructor is very knowledgeable in the subject and very dedicated in his teaching. It has been an honour to be in his class.
• I am impressed at how flexible Stanley was in responding to our feedback. He chose to switch teaching methods halfway through the course - from teaching from slides to teaching from the board. Learning from the board was much better, as the pace of the class was slower and more reasonable. Stanley is a very patient teacher who is always willing to be interrupted during class to answer questions - yet he is able to stay on track. He is good at breaking down complex concepts, and he's able to explain them in good English. He's an excellent teacher who remembers all of us by name and probably the only one of my teachers who can get all of us talking and asking questions in class. Sorry I should also have said this earlier in the course feedback: Something to improve is to separate the coding component and written component from homework, and to move the coding to mini projects. I think it is easier to tackle learning overall in this manner - homework is for consolidation, while projects is for active application unto real life. I also think that it is much better to have consistent quizzes (perhaps bi-weekly?) instead of two huge exams with such huge weightage. It takes the stress off from taking one-off exams, and also helps us consolidate the material more consistently over time.
• Generally much improved pace in the 2nd half. Liked slower teaching, and the HMM materials are quite good quality.But honestly, I still prefer slides. Perhaps something like Caltech's Machine Learning. Suggestion: for future ML courses... all lectures should be recorded... sometimes I really miss out certain key points when Stanley teaches slightly too fast. A video can allow me to rewind and revise better.E-dimension notes can be clearer.
• I preferred his earlier method of teaching with slides, as it gives us something concrete to refer to after class (based on what was covered in class, not the notes). Otherwise, he is still generally a good teacher.
• powerpoint > whiteboard
• Probably the most challenging course in my time here at SUTD, because of the nature of the subject. But worthwhile! If possible, more time could have been given for us to digest each topic, but we are constrained by the short term time.
• Some of the homework questions are really very difficult. =(
• it's too hard i can't copy and listen at the same time i do not know what is going on
• Very proficient in teaching, Stanley have been an awesome teacher in class. He explains theories very clear and he doesnt hesitate to always ask us to ask more questions. Great Job!
• Can there be more concrete examples during lecture
• Sometimes the explanation feels rushed. It is hard to ask questions and clarify when we don't know what we don't understand.
• Instructor is dedicated and knows the subject well. 
• Slides are comprehensive, Lessons are made interesting with various analogies.
• Should explain more on the algorithms used for the homework. The problem with Machine Learning is that every different paper have their different set of conventions, and that algorithms are often skimped over from a mathematical perspective without proper considerations to the pratical performance (e.g. how to make the algorithm run fast using proper data structures, etc.)
• Stanley tries to teach the material the best he can, which I appreciate since the content is rather difficult in itself. The use of slides is definitely appreciated, even small touches like printed handouts, and asking students repeatedly to clear their doubts. The slides can probably still be improved though - e.g. more informative/explanatory as opposed to just brief points. The slides also become quite small when printed 6 per page - perhaps 4 per page would be a better size. In all, good effort in teaching, do appreciate it!
• The content is well structured and understandable
• too much content maybe? feel as if we're rushing onto the next topic without understanding what we've just learntI hope that they will teach or help a bit more on the programming portion for ESD students. 
• More application would be great. A project would be time consuming but it will really help us know what we need to know if we were to implement some learning system in the future. 
• I feel like doing more programming assignments. But argue questions aslo helped me understand the problem.
• it is too hard
• Very subjective. 2hard4me
• This course alone is excellent. However, I still feel that the content of the course is very intense. Especially since there is alot of math to substantiate the theory. Being in the ISTD pillar, I know myself to be very weak at math. This course, coupled with other heavy course from other courses will make a student's workload difficult to manage.
• Perhaps an additional reference material (if the topic is outside of the lecture that we did not already know) would be beneficial, especially for students who need to derive and verify the math to the very core
• It's too difficult.
• Make big programming questions (Like ID3 Decision Tree) Pair Work :) It will make the course load a lot more manageable while still ensuring we learn from implementing the code.
• It's really interesting, but the programming parts are harder to grasps as when trying to program it, we realise there is so many things to tune and unknowns to calculate.
• I feel that they are trying to teach us many Machine Learning Models in a very short period of time. It can be better if they choose to teach lesser models, but with deeper and more thorough explanation of each model.
• I appreciate how Stanley always tries to engage all of us in class. I think that his approachability and willingness to entertain questions has led to more of us speaking up and asking questions. I do think that sometimes Stanley speeds through certain concepts; he should elaborate on them more. One example is the lesson where he explained the backpropagation algorithm - more time should have been spent on working out the math instead of showing us videos on the ALVIN cars. It also feels like we're going at a pretty fast pace. Before we fully digest a new machine learning method, we are moving on to the next already. There is no way I can keep up with the course without going back to read on my own. I do hope the pace will slow down a little more and more time can be spent on explanations for each machine learning methods.
• Course material is rather difficult. Perhaps it could be good, if possible, to provide a greater intuition/more detailed description of the math, easing more into it as opposed to jumping into it straight away. This can help in the understanding of the material.
• Machine learning has been an interesting lesson! It has really broadened my perspective on how machines and code are used in classifying huge amount of information and how applicable this can be in all parts of our lives
• i think this course is very interesting and useful. however, as i came from ESD background, i would like to suggest the contents can be more considerable designed. currently this course ( also homeworks) are more likely to be designed for ISTD students. i had a chance taking MOOC machine learning from Stanford university and their course is designed to students from varies backgrounds. their course are not so technical and the course works are more theoretically based. since machine learning in our school is also open to students from EPD and ESD, it would be nice if the course can be more approachable.
• As a student who did not have long exposure to computing, programming the ID3 algorithm took me at least 10 hours without any results
• It seems to be too difficult for ESD students (especially for the programming part) Hopefully if we put some efforts in it, we will get A.
• The coding is abit difficult
• Hugely disappointed by this course. Steep learning curve, no consideration for students' abilities given the other workload of other courses and programming skills. Concepts are interesting but more effort should be made for students to UNDERSTAND the course materials with DIRECT APPLICATION on the homeworks sets. This course is also extremely unfair and biased.
• SUTD seems to be trying to fit a lot of content into just 14 weeks, unlike other machine learning courses I have seen online. It's not good for learning in my opinion.There seems to be no overview, rather like a meandering through different topics. Probably should start from what we already knew, like regression, from the first week, then progress on from there
• Although this is a very difficult course, this is a very interesting course.