Machine Learning Mario Fritz

Registration for this course is open until Tuesday, 31.05.2022 23:59.

News

17.05.2022

No Lecture on May 18th

There will be no lecture on May 18th. 

I apologize for the late notice.

14.05.2022

Programming Tutorial Notebooks

Dear all,

You can find programming tutorial notebooks for Linear Classification and SVM at https://cms.cispa.saarland/ml22/materials/. Attempt to answer the todos, we will go over the solution on Thursday.

Cheers,
ML22 Team

09.05.2022

Exercise Sheet 4

Dear all,

You can find exercise sheet 4 (on linear regression and linear classification) at https://cms.cispa.saarland/ml22/materials/. Please attempt to solve the theoretical exercises and we will solve them together in class on Thursday.

Cheers,
ML22... Read more

Dear all,

You can find exercise sheet 4 (on linear regression and linear classification) at https://cms.cispa.saarland/ml22/materials/. Please attempt to solve the theoretical exercises and we will solve them together in class on Thursday.

Cheers,
ML22 Team

06.05.2022

Exercise Sheet 4 to be released on Monday

Dear all, 

Theoretical exercise sheet 4 (and programming notebook 3) will focus on Linear Classification and they will be released on Monday. Thursday tutorial is theoretical only and the programming notebook will be reviewed in the coming week. 

You can... Read more

Dear all, 

Theoretical exercise sheet 4 (and programming notebook 3) will focus on Linear Classification and they will be released on Monday. Thursday tutorial is theoretical only and the programming notebook will be reviewed in the coming week. 

You can find video recordings for previous tutorials and solution sheets on cms.

Cheers,

ML22 Team

02.05.2022

Exercise sheet 3

Dear all,

You can find theoretical exercise sheet 3 (Linear regression) on https://cms.cispa.saarland/ml22/materials/. Please attempt to solve the exercises and we will solve them together in class on Thursday.

Cheers,
ML22 Team

02.05.2022

Reminder: No lecture Monday May 2nd

This is a reminder that there is no lecture on Monday May 2nd.

29.04.2022

Programming Exercise Notebook 2

Dear all,

You can find the jupyter notebook for the programming exercise tutorial 2 on https://cms.cispa.saarland/ml22/materials/index.

Cheers,

ML22 Team

26.04.2022

Exercise sheet 1 (Solution) and Video link online

Dear all,

You can find the video link of the tutorial and the sample solution for exercise sheet 1 on https://cms.cispa.saarland/ml22/materials/index.

Cheers,

ML22 Team

26.04.2022

Lecture 3 video available

Video of lecture 3 on Bayesian Decision Theory is online.

24.04.2022

Lecture 3 slides are up - start at 5pm

Slides are available at https://dl.cispa.de/s/sW888NXEPCaLXR7

Please recall that the lecture this time starts at 5pm.

24.04.2022

Programming Exercise 1 Video Link

Dear all,

You can find the video link for programming exercise tutorial 1 on https://cms.cispa.saarland/ml22/materials/index and a revision of the Tutorial Notebook 1. 

Cheers,

ML22 Team

22.04.2022

Exercise Sheet 2

Dear all,

You can find exercise sheet 2 on https://cms.cispa.saarland/ml22/materials/. Please attempt to solve the theoretical exercises and we will solve them together in class on Thursday.

Cheers,
ML22 Team

20.04.2022

Updated slides for lecture 2 and video posted

https://cms.cispa.saarland/ml22/2/Machine_Learning_page_for_registered_students

20.04.2022

Lecture 2 material available - lecture online only

Slides for lecture 2 are available online. Please note that - as announced - the lecture on April 20th will be online only (zoom + video recording).

19.04.2022

Exercise Sheet 1 and Notebook

Dear all,

You can find exercise sheet 1 and coding notebook on https://cms.cispa.saarland/ml22/materials/. Please familiarize yourselves with the notebook and attempt to solve the theoretical exercise sheets. We will go over the notebook and solve the theoretical... Read more

Dear all,

You can find exercise sheet 1 and coding notebook on https://cms.cispa.saarland/ml22/materials/. Please familiarize yourselves with the notebook and attempt to solve the theoretical exercise sheets. We will go over the notebook and solve the theoretical exercise sheet in class on Thursday.

Cheers,

ML22 Team

14.04.2022

Additional recap/study/lookup material online

https://cms.cispa.saarland/ml22/4/Prerequisites_Study_Material

Show all
 

Machine Learning

Lecturer: Prof. Dr. Mario Fritz

Please register on this CMS site for this course, so that we can be in contact. Course information (schedule, material, ...) is available to registered students at the following page as well as the university LSF entry.

Content

In this course we will introduce the foundations of machine learning (ML). In particular, we will focus on understanding the theoretical aspects of ML that have made ML successful in a wide range of applications such as bioinformatics, computer vision, information retrieval, computer linguistics, robotics, etc.

The course gives a broad introduction into machine learning methods. After the lecture the students should be able to solve and analyze learning problems. The lecture is based on the previous machine learning courses offered by Matthias Hein, Peter Ochs, Isabel Valera.

The tentative list of topics cover:

  • probability theory
  • Maximum Likelihood/Maximum A Posteriori Estimators
  • Bayesian decision theory
  • Linear classification and regression
  • Model selection and evaluation
  • Convex Optimization
  • kernel methods
  • Societal Impact of Machine Learning
  • Unsupervised learning (clustering, dimensionality reduction)
  • Introduction to Deep Learning

Prerequisites: 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). In addition, prior attendance to machine learning related courses, eg, Elements of Machine Learning, is considered as an additional useful background.

Organizational Information


Lectures will start on Wednesday, April 13th!

Lectures :

  • Mondays 4pm-6pm E2 5: Lecture Hall 1
  • Wednesdays 4pm-6pm E2 2: Lecture Hall Günter-Hotz

Tutorials:  Thursdays 4pm-6pm HS 003 Geb. E 1.3


FAQs

1. Will the lecture be delivered in English or is it in German?
      The lecture will be delivered in English
2. Will the course be organized in-person or hybrid (via Zoom)?
       It will be a hybrid format.
3. Can the students solve assignments in groups or is it strictly limited to a single person?
      The exercise sheets will be ungraded, and not compulsory to turn in, so you're welcome to solve them in groups or as an individual.
4. Will the lecture be recorded?
      Yes, videos of lectures will be available for download.
5. Are the assignments compulsory in order to take part in the exams?
      No.



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