Elements of Machine Learning Prof. Dr. Isabel Valera, Prof. Dr. Jilles Vreeken

News

22.10.2021

Material for Block 1 available

Dear students,

Although we had released yesterday's slides and exercise sheet 1, the category in the material section was still hidden, so in practice you couldn't find them.

This has been fixed now, and you should be able to find the new material on the... Read more

Dear students,

Although we had released yesterday's slides and exercise sheet 1, the category in the material section was still hidden, so in practice you couldn't find them.

This has been fixed now, and you should be able to find the new material on the section "Block 1 - Intro + Linear Regression" in the "Materials" tab.

Sorry for the inconvenience,
Your TAs

22.10.2021

FAQ: I passed all the assignment sheets of EML last year - do I need to do them again this year? Yes

Dear all,

 

we have received multiple requests of students, who have successfully passed all assignments of EML last year.

To keep things fair for this year's students, pre-requisites do not carry over. If you want to take this year's exams, you will need... Read more

Dear all,

 

we have received multiple requests of students, who have successfully passed all assignments of EML last year.

To keep things fair for this year's students, pre-requisites do not carry over. If you want to take this year's exams, you will need to do this year's assignments. No exceptions are granted.

 

All the best and a great start of the course,

Your TAs

 

22.10.2021

Please do not contact the professors directly - Your TAs are here for you!

Dear all,

we are excited about the overwhelming interest in the EML course this winter semester!

 

If you have questions for the course, please not contact the professors directly.

1. Check CMS, if the information has already been placed there.

2. If... Read more

Dear all,

we are excited about the overwhelming interest in the EML course this winter semester!

 

If you have questions for the course, please not contact the professors directly.

1. Check CMS, if the information has already been placed there.

2. If it is not on CMS and

2a. It is a question of general interest, the CMS forum is the place to ask. Please read and follow the rules of posting there.

2b. If is a personal question, please contact us this and only this email: eml-ta@lists.saarland-informatics-campus.de.

 

We assure you to answer all your questions - at the same time we ask for your understanding that we receive a lot of e-mails and therefore please be patient in receiving a reply.

 

Thank you! Looking forward to a great semester with you all,

Your TAs

 

19.10.2021

Lecture 0 - out now!

Dear all,

 

welcome to Elements of Machine Learning in winter semester 2021/22!

We are happy to inform you that we have released Lecture 0 - Introduction. Please find the slides under the CMS Materials section and the recording here.

Please find the... Read more

Dear all,

 

welcome to Elements of Machine Learning in winter semester 2021/22!

We are happy to inform you that we have released Lecture 0 - Introduction. Please find the slides under the CMS Materials section and the recording here.

Please find the Tutorial 0 (introduction to R) here and Assignment 0 (self-assesment) under the CMS Materials section.

Please find the schedule of the course including tutorial and lecture dates (tentative) in the CMS Timetable section.

 

Best,

EML Team

 

1. Oganization of the course: Please read this information carefully. A pre-recorded lecture (Lecture 0) with all organizational details is available for you (see News). If you remain with questions afterwards, kindly join us in the first tutorial, where you will be able to ask questions.

2. Please fill out our survey: In order to have a good estimate of the number of students attending in-person sessions for help tutorials, we kindly ask you to enter here your preferences (you need to be registered in the course).

3. Coronavirus: Please find here the university FAQ for attending lectures in person: https://www.uni-saarland.de/en/page/coronavirus/faq.html All participants in a face-to-face event at the university (in teaching and non-teaching) must provide proof of 3G according to the current provincial regulation on the Corona pandemic, i.e. either complete vaccination, recovery or a negative test (twice per working week). Proof is provided by using and truthfully stating in the Staysio app; for this purpose, all premises used for attendance events must be mapped in the Staysio app. Alternatively, you can register via the web application (English version) described here in the lower part. Otherwise, a German Apple/Google account can also be used for the download.Should participants not be able to register themselves via smartphone, they are obliged to submit their contact details and 3G proof to the event management immediately at the beginning of the event using the following form (data protection declaration). If the form is used, it must be collected by the lecturers, kept in accordance with data protection regulations and destroyed after a period of four weeks.

4. I took EML last semester and I passed all the assignment sheets - do I need to do the assignments again this year, to be able to register for the exam? Yes for reasons of fairness, you will need to do them again.

5. Please abstain from contacting the professors directly. If you have questions, check this page, check the forum, post your question in the forum and - last resort - contact us via  eml-ta [at] lists.saarland-informatics-campus.de

6. Practical submissions only in R. As clearly explained on both this website and in Lecture 0 (Organization) we will only consider practical assignment submissions in R.

7. What is the difference between the EML (this course) and ML (last summer semester) course?  ML is more advanced, comprehensive and comes with more mathematical depth than EML. It is likely that ML be offered again in Summer 2022, but not yet decided. So unfortunately we cannot yet guarantee this at the moment.

 

For more FAQ please check the Forum.

 

Elements of Machine Learning

In this course, we will discuss the foundations—the elements—of machine learning. In particular, we will focus on the ability of, given a data set, to choose an appropriate method for analysing it, to select the appropriate parameters for the model generated by that method and to assess the quality of the resulting model. Both theoretical and practical aspects will be covered. What we cover will be relevant for computer scientists in general, as well as for other scientists involved in data analysis and modelling.

This course replaces the course Elements of Statistical Learning, and will be held in English.

 

Prerequisites: The course is targeted to students in computer science, bioinformatics, maths, and general sciences with a mathematical background. Students should know linear algebra and have good basic knowledge of statistics, for example by having taken Mathematics for Computer Scientists I and II (for linear algebra) and Statistics Lab or Mathematics for Computer Scientists III (for statistics).

 

Important resources to start the course:

  • Introduction tutorial to R. Make sure to check this pre-recorded tutorials, recorded by Osman Ali Mian, in order to get a nice and short introduction to the R language that you will need for the tutorials during the course.
  • Self-test. You can use this exercise sheet to evaluate whether you have the required background to attempt EML. For the coding part you will need to also download the ozone.Rdata dataset.
  • Lecture 0 - Organizational details. Recording by Prof. Vreeken on the organizational details of the course. Make sure to check the video and the slides, which you can find in the Material section on CMS.

 

Registration

Students for which EML is a required part of their Bachelor programme can take it as a Basic Lecture, while all other students (Bachelor or Master) can take it as an Advanced Lecture. There is no difference between the two, except for how it will be listed on your transcript, either way you receive 6 ECTS for successfully completing the exam. Obviously, you can only get credits for the course once.

 

Contact

We have prepared a forum for you to discuss your questions and find study groups during the course. You can find it on the top bar on CMS.

Please before reaching out to us,

1. Check, if the information is available on this CMS website, in the lecture material, or in the recommended reading.

2. Check, if the information is available in trusted online sources and check with your fellow students.

3. Check the discussion forum, if your peers have already asked the question.

4. Post your question in the online forum using the correct category and subject line. This allows your peers to learn from your questions.

Only in exceptional cases, where your question is private/personal, please email us to: eml-ta  [at] lists [dot] saarland-informatics-campus [dot] de. Please do not send us private message via the forum. We will not be able to answer them.

 

Organizational Information

Lectures

Lectures will start in the week of October 18th!

In the lectures we will cover the main theoretical aspects of the course.

Lectures*:  Thursdays from 16:15h to 18:15h (Gebäude E2 2 - Hörsaal 0.01 (Günter-Hotz-Hörsaal) )

Lectures will also be accessible online. More information will follow.

 

Tutorials

During Solution tutorials, the TAs and tutors will present the correct solution and answer questions about them. These tutorials are only held online. During the Help tutorials, TAs and tutors give individual and group-wise assistance. These tutorials will be held both online and in-person on campus.

Please make sure that you submit a filled-out copy of the code of conduct (you can find in the material section) for each assignment.

Tutorials*: Group A: Mondays 12:15-13:45h, Group B: Tuesdays 12:15-13:45h.

You will get assigned to one of the groups based on your preference that you can indicate, when you register for the course. Please attend only the assigned tutorial.

Privacy disclaimer: We have decided to use Zoom for the tutorials, as it provides superior functionality and usability for lecturing, including seamless live interaction and smooth integration of a whiteboard. We thus encourage you to join the Zoom with your real name, your camera on, and ask questions verbally. However, this is of course voluntary. If you are concerned about privacy, we encourage you to enter the Zoom meeting under a nickname or pseudonym, and use only the textual chat for communication.

 

Evaluation

The course will be evaluated in a final exam (which will most likely take place in written form). To be eligible to participate in the exams, you will need to have cumulatively scored 50% of the points for the theoretical exercises and 50% of the points for the programming exercises. To participate in the exams, you will need to register at least one week before the exam via the LSF/HISPOS system of Saarland University.

We will inform you, when the exam registration on HISPOS/LSF opens. If you are not able to register for the exam in HISPOS/LSF, please send us an email with your full name, matriculation number and study program to: eml - ta [at] lists [dot] saarland-informatics-campus [dot] de

 

Schedules

Please navigate to: Timetable for a detailed schedule of the lectures and tutorials. Please navigate to: Information > How to access the lectures? to find the respective rooms and links.

 

Lectures*:  Thursdays from 16:15h to 18:15h (Gebäude E2 2 - Hörsaal 0.01 (Günter-Hotz-Hörsaal) & oline )

Tentative Lecture Schedule
Oct 21 Statistical Learning
  28 Linear Regression I
Nov 4 Linear Regression II
  11 Classification I
  18 Classification II
  25 Resampling & Model Selection
Dec 2 Dimensionality Reduction
  9 Unsupervised Learning I
  16 Clustering
Jan 6 Matrix Factorization (online)
  13 Beyond LInear
  20 Tree-based Methods
  27 Support Vector Machines
Feb 3 Neural Networks
  10 Wrap-up with Q&A

 

 

Tutorials*: Group A: Mondays 12:15-13:45h, Group B: Tuesdays 12:15-13:45h. (Help hybrid, Solution online)

Tentative Tutorial Schedule
Assignment Type Tutorial A Tutorial B
  Intro 25.10.21 26.10.21
1 Help (hybrid) 03.11.21 (online) 02.11.21
1 Help (hybrid) 08.11.21 09.11.21
1 Solution (online) 15.11.21 16.11.21
2 Help (hybrid) 22.11.21 23.11.21
2 Help (hybrid) 29.11.21 30.11.21
2 Solution (online) 06.12.21 07.12.21
3 Help (hybrid) 13.12.21 14.12.21
3 Help (hybrid) 10.01.22 11.01.22
3 Solution (online) 17.01.22 18.01.22
4 Help (hybrid) 24.01.22 25.01.22
4 Help (hybrid) 31.01.22 01.02.22
4 Solution (online) 07.02.22 08.02.22

 

 

Assignment Deadlines
Assignment Published Hand-in (14:00h CET)
1 October 21, 2021 November 11, 2021
2 November 11, 2021 December 02, 2021
3 December 02, 2021 January 13, 2022
4 January 13, 2022 February 03, 2022

Note: Deadlines to hand in are 14:00h CET.

 

R resources

R (version 3.2.3) is installed on the CIP pool computers and can be started by invoking R from the command line.

The official website of the R project is r-project.org. You can download R for Windows, Linux and Mac from there. Additional packages, documentation and tutorials are also available for download from the official website. Useful manuals and tutorials include:

  • R for Beginners by Emmanuel Paradis. Especially relevant for us are chapters 1, 2, 3 and 6.
  • An Introduction to R - the standard R introduction. This is a very detailed manual; it is therefore quite lengthy.

The CRAN Contributed Documentation lists many other tutorials for R beginners and advanced programmers.

You can also check out RStudio, an open-source IDE for R.

 

Bibliography

The course will, by and large, follow the book "An Introduction to Statistical Learning with Applications in R" [1]. At times, the course will take additional material from the book "The Elements of Statistical Learning" [2]. The former book is the more introductory text, the latter book is more advanced. Both books are available as free PDFs. We strongly encourage you, though, to acquire at least the first book in print. If you need to brush up on statistics, we recommend "All of Statistics" [3]. All books, as well as further background literature, are available via the library in a so-called Semesteraparat.

[1] James, W., Hastie, T. & Tibshirani, R. An Introduction to Statistical Learning with Applications in R. Springer, 2013.
[2] Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning. Springer, 2009.
[3] Wasserman, L. All of Statistics. Springer, 2005.

 

For selected lectures, we will identify interesting optional reading, such as relevant recent research papers. These we will make available here.

[4] van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. Journal of Machine Learning Research, 9:2579-2605, 2008.

 

Acknowledgements

EML is based on The Elements of Statistical Learning as developed by Thomas Lengauer. We thank him for kindly sharing both materials and experience.



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