CS-498 Applied Machine Learning

D.A. Forsyth --- 3310 Siebel Center

Office Hours Time: TBA, Location: TBA

DAF hangs in sidemount scuba gear, with left tank in slightly poor trim, over a small submerged wreck interior of a sunken bus, in gloomy lighting, with DAF in sidemount scuba gear looking to the right of view, and pointing a light

Alternative locations may be available

TA's:

Important; Important; Important

Announcements page - check this frequently!

On it, you'll find the homework submission policy!

A straw poll

here

Contact policy

With multiple versions of the course and nearly 400 students, I'm quite distracted and am focusing on content preparation. Generally, please do not bring DAF an issue you haven't already raised with a TA.

Questions I've been getting a lot

Getting into the class In the past, we've been able to admit everyone who wanted to get into the in-person version of the class after the first rush settled down. Will this be true this semester? who knows? not me. PLEASE do not come and tell me that you really want to get in, or your cat died and its last words were to take the class, or something. I'll try to admit everyone, but can't concentrate on doing that if all try to tell me why they want to get in.

There will not be an overflow section. This means that if enough people drop so that there is space in the room, you'll get in; if not, you won't. In the past, people have started on the first homework and been safe. I can't guarantee this will be OK this year.

The only 4-hour version is online. Sorry, too many students and versions already.

Can I audit? The main resource limits on the physical class are physical seats in the room. We cannot have an overcrowded room. If physical seats are open, sure (I'm always happy to have an audience); but please don't take a seat that should be occupied by someone who is registered

What is the difference between the 3 hr and 4 hr versions? 4 hour versions (some online students; MSCS-DS students) will get an extra assignment later in the course.

Important contact advice

A really common question is: how do I do something in R? Usually, I get the answer to this by searching; I use Google, but you may have a preferred search. If you ask me or a TA this question, and we do this it in front of you successfully you should feel a little embarrassed cause you could have done this for yourself. Warning: we will embarrass you in this way; it's better to do this sort of thing for yourself.

Office Hours

You should contact a TA for your version of the course using that version's appropriate method. There will be Zoom office hours for the MSCS-DS course. For the in-person version of the course, go to the appropriate location

Evaluation is by: Homeworks and take home final.

I will shortly post a policy on collaboration and plagiarism

 

 

Entrance survey

please complete this. I intend it to be anonymous (but don't fully understand google forms, so...) and it will help me know what you know already. Please don't fill in lots of forms to bias, etc.

Homeworks

A total of 9 homeworks will appear here. There will be no final exam - one homework will be designated a take-home final.

Censored rubrics

Here are the criteria we will use in grading with some gory details omitted

Drafts of future homeworks

These are drafts of homeworks I intend to release with attached due dates. This helps people work ahead, BUT you need to keep in mind that I'm not guaranteeing that this wording is what will appear in the final homework. Details might change, but changes should be small. Due dates are approximate, and may change.

Syllabus:

I will start at the beginning of the textbook and proceed to the end, covering approximately one chapter per week. You'll notice there are 14 substantive chapters and 15 weeks; this is to allow a little spreading out, but in week N I expect to be in or close to chapter N. Read the textbook. I wrote it specifically for this course, AND it's free. I will split time in lecture between sketching important points described in the text, and solving problems. If you haven't read the text, this might be quite puzzling.

Important This will change as I purge typos, etc. In the past, people have brought the pdf with them on mobile devices. If you print it, you'll have to do it again.

Required Text:

Applied Machine Learning, D.A. Forsyth, (approximate 9'th draft)

Piazza link

for this course (which is now right, I believe)

Piazza duty list

Generally, TA's are expected to spend much of their time on piazza. But we have a system of on-duty and off-duty days. On these days, you'll find much of the piazza action from the named TA (we hope!)

I'm a video star! (or at least, I have been filmed)

Here's what appeared on my screen

broken links fixed (I think)

low resolution movies of the class screen; good motivation to remind me to adjust zoom on projector, swap screens, etc.

higher resolution movies of the class screen, for those who like more pixels.

Backup Material:

Probability and Statistics for Computer Science, D.A. Forsyth

Cover image for Probability and Statistics for Computer Science

 

Code fragments I showed in class:

I've cleaned some of these up a bit, but they're not intended to be production code, etc; just to show some R tricks. Among other things, these codes contain known errors!

R resources: