IAML-PG2: Course Information

Information for all IAML students

The goal of this course is to introduce students to basic algorithms for learning from example data, focusing on classification and clustering problems. This course is intended for MSc in ATFC and MSc in FTP students.  It is delivered using an Inverted Classroom method.

The syllabus for this course is defined by the lecture slides, the tutorial questions and the labs. Any items from any of those sessions may be examined on. The required understanding of a subject may go beyond what is simply presented in the slides: you should know how to apply it and the more detailed context. The course texts and the videos can help with that.

Class Meetings

IMPORTANT INFORMATION: This course is not taught in the traditional lecture style.  The expectation is that you take more control of your education in this course. This means that you will have about 20 hours of video to watch in your own time. This material is assessable, except where noted. It is very important that you watch the videos and do the associated quiz for the class meeting topics at least a day before the class. Each class will consist of several parts: 1) discussing any questions about the videos that you either suggest in advance or raise in class on the day, 2) simple non-assessed exercises to explore the issues raised in the videos that you have just watched, 3) exercises or examples based on quiz questions that were difficult, and 4) examples going further into depth in specific subtopics.

Topic Content

Each topic listed in the Topics section has a set of subtopics in a particular order, and it will make most sense if you go through them in this order.  For each subtopic, there are the following resources:

  • PDFs of the slides used in the video for the subtopic.
  • Video for the subtopic

For the topic as a whole, there is also the following:

  • Consolidated PDF of all the subtopic slides.
  • Video playlist for all the subtopic videos - this can be easier if you want to watch all the videos for a topic in one session.
  • In some cases, links to external resources to provide additional information and/or other perspectives on the topic.
  • A self-assessment quiz for topic content - you may take this multiple times and it is intended for you to assess how well you have learned and understood the topic content.

Other Resources

Past years' exam papers are available online. Solutions are not available.

The lecture notes from the old Learning from Data course are useful, although they contain more mathematical detail than we are expecting for IAML.

A Few Useful Things to Know about Machine Learning by P. Domingos

Piazza Q&A

Piazza is the place to ask questions about the course materials: topics slides and videos, the labs, tutorials and the assignments. We encourage students to answer questions if you can - it is a great learning experience to explain something to another student.   If you have issues that should be kept confidential, then of course please do email the course lecturer, but otherwise use Piazza - it is more efficient and it benefits everyone.  Please note that you are able to ask questions anonymously to other students on Piazza - but this does not make your question anonymous to lecturers and TAs.

Click here to access piazza.

Coursework

There is one coursework, worth 30% of the overall mark.

Note that the Labs are strongly advised as preparation for the assignments - each Lab introduces you to the libraries and how they are used that will be assessed in the correspondingly numbered assignment.

See the Labs & Assessment pages for details.

Tutorials

Tutorials will be in weeks 3, 5, 7 and 9.  You will be allocated to a tutorial slot by the ITO and if you cannot make the time then get it adjusted by the ITO.  See the Tutorials section.

Labs

Assignment-related labs will be in weeks 2, 4, 6, 8, and 10 with an optional introduction to Python and packages in week 1.  See the Labs & Assessment pages.

Exam

The exam, worth 70% of the course mark, will be in the April/May diet.  See the Assessment page.

Office hour

TBD

Enrolling in IAML

If you can see the Announcements link on the left menu, you are already enrolled in IAML!

If you are staff or student at the University of Edinburgh, you can self-enrol in this course on Learn, but NOT for credit.  See below for details on how to do this.  Self-enrolling in Learn gives you access to all the online materials but it will not get you included in Lab or Tutorial groups.  To be included in Lab and Tutorial groups you need to be enrolled for credit in Euclid.

If you want to take IAML for credit, you must get your personal tutor or supervisor to enrol you in Euclid, which will then automatically enrol you here on Learn.

How to self-enrol in IAML in Learn:

  • Log into LEARN: https://www.learn.ed.ac.uk.
  • Select 'Self-Enrol' tab, and Browse Course Catalogue.
  • Search for 'Introductory Applied Machine Learning'
  • Move your mouse over the 'Course ID' and click the Carret symbol and choose 'Enrol'.

The IAML course should now be listed under your 'My Learn' tab. This is where the class material is.

License
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