Registration for this course is open until Friday, 31.05.2024 23:59.

News

First lecture happening today at 12:15 at G√ľnter-Hotz lecture hall

Written on 18.04.24 by Isabel Valera

Dear all,

The Introductory lecture to ML core course will be happening TODAY at noon. You can join the lecture either in person (preferred option) or via Zoom. 

In addition, due to several requests, the lectures will be recorded and shared with the students via CMS!

See you soon,

Prof.… Read more

Dear all,

The Introductory lecture to ML core course will be happening TODAY at noon. You can join the lecture either in person (preferred option) or via Zoom. 

In addition, due to several requests, the lectures will be recorded and shared with the students via CMS!

See you soon,

Prof. Isabel Valera

 

Schedule, exam dates and first materials released

Written on 21.03.24 (last change on 21.03.24) by Jonas Klesen

Dear ML students,

welcome to this year's iteration of the ML core lecture! We have just uploaded a tentative schedule, the exam dates, the tutorial timeslots, as well as some materials with which you may refresh your prerequisite knowledge for the course.

The first lecture will be on April 18th… Read more

Dear ML students,

welcome to this year's iteration of the ML core lecture! We have just uploaded a tentative schedule, the exam dates, the tutorial timeslots, as well as some materials with which you may refresh your prerequisite knowledge for the course.

The first lecture will be on April 18th and cover course logistics and an introduction to the topic. 

 

Best,
ML Team

Machine Learning

 

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 from a theoretical point of view. 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 and Peter Ochs.

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: The course is targeted to students in computer science, bioinformatics, math, and related areas 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). In addition, prior attendance to machine learning related courses, e.g., Elements of Machine Learning, is considered as additional useful background.

 

Organizational Information

Lectures:

Mondays 14:15h at Building E2.2  -- Günter-Hotz Lecture hall I (0.01) &
Thursdays at 12:15h  at Building E2.2  -- Günter-Hotz Lecture hall I (0.01) 

Zoom link to join the lectures online (lectures will be recorded and shared with students):

Meeting ID: 822 9140 8214

Passcode: 889008

Lectures will start on April 18th! 

Tutorials (to pick between):  

Wednesday, 12-14 in Günter-Hotz Lecture hall (0.01 of E2.2)
Thursday, 16-18 in lecture hall 003 of E1 3
Friday, 10-12 in lecture hall 003 of E1 3

Exams: 

Main exam: August 5th from 2 pm - 5 pm
Re-exam:   September 30th 2 pm - 5 m

 

Schedule:

 

 

Bibliography

[Bishop] Bishop, C. M. Pattern recognition and machine learning. Springer, 2006

[DSH] Duda, R. O., Hart, P.E., and Stork, D.G. Pattern classification (2nd edition). Wiley-Interscience 2000

[Boyd]   Boyd, S., Boyd, S. P., and Vandenberghe, L. . Convex optimization. Cambridge university press, 2004

[eML]    Hastie, T., Tibshirani, R., and Friedman, J. The Elements of Statistical Learning. Springer, 2009.

[Kernels] Smola, A. J.,  & Schölkopf, B. Learning with kernels. GMD-Forschungszentrum Informationstechnik, 1998.

[DL]   Goodfellow, I., Courville, A., and Bengio, Y. Deep learning. MIT press, 2016.

[DL]   Simon J.D. Prince Understanding Deep Learning. MIT press, 2023.

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