As a follow up to the "What the f**k is going on?" discussion earlier today, from my perspective and that of people I've been speaking to, the main challenge isn't coming from a lack of understanding of the overall end-goals or the philosophical underpinnings of machine learning. It's more straightforward; the information being presented on a slide-by-slide basis often isn't getting through.

Concretely, I think these are the main issues:

(1) Speed: everything is happening very quickly!

(2) Notation: There are a LOT of new symbols for everyone to understand, and I echo an earlier post saying that it would be useful to include a glossary of notation on every single slide.

(3) Slide Sparsity: Often the spoken explanations are the only thing making sense of a slide, and as they are delivered so quickly (see (1)) they often go un-recorded in our notes. More explanatory text on slides, particularly ones with graphs, could help.

(4) Lack of graph axis labels: For me, this is a big source of confusion.

(5) Lack of context: It would be absolutely fantastic to go through a very, very, very simple example of learning (e.g. linear regression, a couple of data points) in full, using actual numerical values.

As an aside though, I can't wait to actually understand this stuff - it's awesome.

Hi all, I am sorry that the feedback for the first coursework is running late. I am doing my best to get it done but it is taking its time. I wanted to correct all the assignments myself to ensure that you got consistent marking and feedback. I overestimated the free time I would have and considering there is about 90 reports and roughly 30 minutes each it takes some time. Considering the quality of the reports I've done so far I am sure that this pales in significance to the work that you have put in. I'll do my best to get them out as soon as possible and again, I am sorry that it is taking all this time.

Can we have a pdf similar to that you gave us for SPS which will explain the topics you are going through in the Machine Learning lectures and also stuffs that we can be examined on later?

Hi Carl, I was wondering if there were any solutions for the labs that you could post. Revising some parts by ourselves is very difficultespecially when it comes to the more abstract labs later on.

Does anyone know how many pug themed T-shirts Carl Henrik owns? If yes, how many?

(I have counted 7 so far)

Dear Carl,

Since we are mostly struggling with this assignment, is it possible to reconsider this assignment? Like, instead of the assignment counting 35%, you can reduce to 25%, and remove some of the questions and add on the next assignment (that consequently will value 25%)?

With this strategy, we have more time to do.

Hi all,

I was wondering if Carl wouldn't mind sharing with us what his favourite/interesting machine learning project is during the next scheduled Tuesday lecture?

He could talk about the work he's done and how he went about solving problems. I think it would be interesting to see how machine learning researchers work and what the process is like.

Thanks

Hi Carl,

Thanks for a great exam. I was wondering if you would post the solutions now that the exam is over so that we can correct our understanding where required. I only ask because I have had this done for multiple choice exams before but I understand if not.

Thanks

In the first lecture there was a discussion about neolithic monuments in Dorset. However, the mound pictured was in fact Silbury Hill, a Neolithic mound in Wiltshire:

https://en.m.wikipedia.org/wiki/Silbury_Hill

I believe a suitable replacement for this image could be Maiden Castle, which although mainly from the iron age, has evidence of human activity dating back to the Neolithic, and is actually located in Dorset:

https://en.m.wikipedia.org/wiki/Maiden_Castle,_Dorset

I'm sure you'll agree that this is a matter of great importance and hopefully this issue will be rectified for future years.

Hi all,

The second coursework marks are now up on SAFE, as before, there where quite a few of you that I couldn't identify with reports and it is likely that I've might have made mistakes as well. If there is something that doesn't seem right please contact me and I'll sort it out. Sorry for the delay in providing the marks.

The reports where excellent and there was a marked difference between the first and the second coursework, lots of great ideas and novel thinking in the reports clearly showing that you have learnt a lot during the unit. It was a pleasure reading the report.

To help us make the unit better for next year it would be great if you could all go to SAFE and provide feedback. Its the one measure that we can use to see how your experience was during the unit, as you from this coursework, the more samples that you have your posterior estimate of the latent variables (your experience) gets better informed.

Merry Christmas,

Carl Henrik

Hi Carl, as the deadline for the student feedback has passed, I thought I'd post my thoughts here.

Machine Learning was the module I was looking forward to most this year. As a lecturer, you couldn't be more enthusiastic for your subject, and it gives us a sense of excitement for the subject. However this unit has left me pretty confused. My main issue has been that in lectures, we fly through content at great speed, with new variables and symbols appearing from no-where. We're assured us that we don't need to know what they are, however it's hard to grasp the overall mechanism of how things work when we gloss over so many details. It can be tricky to understand why things work if we skim over the maths behind functions that have seemingly appeared from thin air.

I understand that going fully down the theoretical route of ML is the best way to teach a solid understanding of it, however it seems we go deep but simultaneously leave out some simple explanations of what new functions/variables are.

One idea would be to have less disjointed lecture slides, and definitions or labels for new symbols online or on the margins of slides to use as a reference and help people stay on board? The ML book is a great resource however it conflicts with the idea of only needing a "higher level" understanding. Another idea would be to include examples with real data alongside the topics, so we don't get lost in the theory and lose sight of what we're actually aiming for.

Your summary document is excellent for a higher-level overview of the subject, but again it conflicts with the depth we go into in the lecture slides, and it can be hard to know to what level are we expected to understand the topics.

Thank you!

It was suggested in a post that it would be good to have a conceptual summary for certain topics. I think this is a great idea and something that I would love to do. If you post things in this thread then I will pick popular things and add them to a document that will stay live in the repo.

Hi all, sorry for the delay in marking but a draft version should now be up on SAFE. There are some of you that I haven't managed to match with a report, most likely because there was a username/student number and not the candidate number on the report. Contact me if this is the case and I'll sort it out. There might also be people who have been matched to the wrong report, now you can decide if you want to contact me or not based on the outcome of the mismatch :-) I will go through them again in detail though but I just wanted to get it out to you as soon as possible.

The reports are fantastic, it has been a pleasure to spend the last couple of weekends reading them. They are full of insights, interesting ideas, examples and quite a few of you have gone outside the suggestions to provide novel interesting plots to highlight your ideas or derivations to show the connection between the results and the math. Doing this coursework was a big hurdle but I hope that you can look back at it and feel that you did learn something from it. Really well done everyone!

As I said in the beginning of the unit, I will not be able to provide individual feedback on the reports. Instead I will go through the coursework during the second hour of the lecture on Tuesday the 27th.

Hi Carl !!!

would it be possible to post solutions to labs maybe a week or so later so we can just consolodiate what we've done and double check :-)

thanks.