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Rating

Total average

74.8/100

Learning Materials ( 79.7 )
Learning Activities ( 64.3 )
Blackboard Management ( 80.3 )
Course Content ( 72.7 )
Course Structure ( 74.3 )
Contact Availability ( 84.7 )
Course Difficulty ( 67.3 )

Reviews (3)

Anonymous
   
COMP4702 – Machine Learning 72.0

CONTENT: I am an electrical student and took this elective in my second year out of interest. The content of the course is very diverse: lots of different topics are covered. This is a problem in the sense that they mostly aren't covered really in-depth, but is also good in the sense that it gives you a very good overview of the discipline of machine learning.

Textbook reading is required (online textbook resources are supplied for free), and the content in them is quite technical. You will need to have a decent knowledge in statistics and it would be good to have done a stats course, such as STAT2202 before. Calculus is essential.

The content covers regression, classification, density estimation, dimensionality reduction, non-parametric methods, clustering methods, neural networks, support vector machines, Bayesian networks, Bayesian learning and Gaussian processes, along with overarching themes and topics common to all areas of machine learning like overfitting, underfitting etc. Neural networks are probably the topic that is covered the most in depth, which most students would probably appreciate.

LECTURES: The lectures weren't very good. Marcus doesn't give you the impression of really knowing that much about the topics and generally just stumbles his way through the textbook, some notes, or papers on the screen. He does seem unprepared quite often. Sometimes he reads the textbook and then gets confused about it. He should be making sure his thoughts are all ordered before the lectures. At the same time, it's probably not a good idea to skip the lectures. The lectures come with pre-reading - I would make sure to do this.

PRACTICALS: The practicals were generally good. They may be hard for some to complete in the two hours, but this isn't a problem. You can complete them at home by using the student license for MATLAB. Prac questions are chosen randomly as assignment questions which must be handed in every few weeks. I would not leave these questions to the last minute as some of the questions might require time for you to understand and work through, especially if you haven't been paying attention to the lectures.

FINAL EXAM: The final exam was fairly easy. Time was not an issue for me, I finished it 40 minutes early. You get to choose some of the questions. As long as you have been paying attention in the course, doing the practicals, reading the textbook, and revised previous exams, you should be fine.

SUMMARY: The course is not very difficult, but could have probably been structured a bit more nicely, especially if the lecturer didn't rely so much on the textbooks. A downside is that you don't really come out of the course feeling like you know as much as you would have learnt in a 2 unit course. This is probably because of the variety of topics that are covered, a lot of which are very intuitive.

NOTE: I took this in 2018, not 2017 - but there's no option for 2018!

Semester taken

Semester 1 - 2017

Your program/major

BE (Electrical)

Is lecture attendance necessary?

Not compulsory, but you should attend anyway

Is the textbook necessary?

Yes, but they provide online versions

Positives
  • Good overview of machine learning
  • Not very difficult
  • Practicals were decent
Negatives
  • Lecturer doesn't give the impression of knowing much
  • Content is not very in-depth
Posted on July 2, 2018 7:30 pm
Anonymous
   
COMP4702 – Machine Learning 63.3

Okay course with content covering machine learning basics but if you want to get into deep learning and possibly be part of the AI boom this course is not going to get you there. You will be spending most of your time doing stats in Matlab and making trivial predictions and models, which might let you down if you expect to be taught how to build AI that magically works.

The lecturer doesn’t really explain much in lecture so you need to do a lot of self learning to understand hard concepts. Most of what he talks about in lecture is super easy to miss unless you are on top of the content he’s talking about, which is a little ironic. I would suggest watching tutorial videos online to help you understand the content better.

Assignments are fairly doable but the requirements can be vague due to the lack of a marking criteria. It’s hard to know what to write about for short response questions and you often find yourself losing marks because you can’t guess exactly what needs to be included in your answer.

Semester taken

Semester 1 - 2017

Your program/major

BE

Is lecture attendance necessary?

No

Is the textbook necessary?

Yes

Positives
  • Lecturer is friendly.
  • Doable assignments.
Negatives
  • Hard to learn much (if anything) from lectures alone
  • A lot of self learning required
  • You have to be comfortable with stats and probabilities.
Posted on June 6, 2018 5:02 pm
Anonymous
   
COMP4702 – Machine Learning 89.0

This course was really interesting and fun. Although there is a fair bit of statistics at the start, the programming exercises in the practicals give you a really good insight into the different methods of machine learning. I found the assessment not to be too time consuming however still challenging. I did the summer course CSSE3080 just before this which covered some of the material in this course so the first few weeks were pretty easy. Marcus is approachable and very reasonable with assessment. The assignments can mostly be done in the practicals. I found the reading assignments really interesting and were reasonably easy marks. The exam was challenging however similar to previous years. Leading into this course the programming subjects i had done were CSSE1001, CSSE2010, CSSE3080. As long as you are comfortable with MATLAB and linear algebra though you wouldn't need that much programming experience. Awesome course, definitely recommend it!!

Semester taken

Semester 1 - 2016

Your program/major

BE Electrical Engineering

Is lecture attendance necessary?

Yes if you want a good mark

Is the textbook necessary?

Yes

Positives
  • Marcus is very approachable, happy to answer questions, so are his tutors
  • gives really good insight into the "real world" applications of machine learning
  • Practicals are really useful, learnt most through them
Negatives
  • course content is released on the fly
Posted on August 23, 2016 9:52 am

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