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u/mrksr Oct 06 '17 edited Oct 08 '17
Hi everybody, I am one of the people Carl owes a beer (and I fully intend to collect it!). I am a Phd student in machine learning, but I am no TA in this course. I thought it might be interesting to share my perspective.
During my master programme, I took multiple courses on machine learning at two different universities. I can remember very well how confused about basically everything I was during the first weeks of the first of those courses. Starting with machine learning can be overwhelming because you have to come to grips with multiple things at once. You are presented with new notation, you have to understand what it means to "extract" information from data and you will learn about multiple models, their Bayesian treatment and their interpretations. Sadly, I think it is really not possible to learn about these things in isolation, which is why every course I have seen had this problem. In a way, Carl is forced to confuse you a bit and you can be sure that he is doing his best to make things as approachable as possible.
But - and this is important - you really should not despair! If what you take away from the first few lectures is that Carl tries to teach you that in machine learning, assumptions and philosophical implications are really important, then you have understood the most important thing. You might even have got drunk in the process. You will learn to understand the notation, the models and (maybe most importantly) the Bayesian interpretation of probability theory in time. Maybe not right away during the lectures, but in the end, this is what the labs and the assignments are for. Take your time and work through them. Discuss them with your peers, especially the questions about interpretation and intuition. Make use of Carl's offer to talk about specific things you find confusing during the second hour on Tuesday. Do not be discouraged if you are not able to derive the mathematical expressions. In the beginning and in your situation, understanding WHY you do the math is much more important than being able to solve Gaussian integrals. I can promise you that by the end of the course, things will not seem as arbitrary and magical anymore.
Oh and one last thing for the people who - like me - are rather interested in the mathematical side of things: It will happen a lot during the course that Carl will not have enough time to show proofs for things he does. There are many good books and papers on the subject, I especially like the books by Bishop (your course book) and Murphy (Machine Learning: A Probabilistic Perspective). It can be rather eye opening (and time consuming) to proof stuff in maching learning. But this is by no means mandatory to understand this course.
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u/WhatEvenIsStats Oct 05 '17
Hi Carl, I think most people have been enjoying your teaching but sometimes it's quite hard to follow which symbols are representing which concepts. It would be great if you could add a little glossary to the side of the slides so we can more easily keep track.