Seminar: Seminar on Machine Learning - Details

Seminar: Seminar on Machine Learning - Details

Close message box
You are not logged into Stud.IP.

General information

Course name Seminar: Seminar on Machine Learning
Semester WS 2024/25
Current number of participants 5
maximum number of participants 8
Home institute Computational Biology
Courses type Seminar in category Teaching
Next date Tuesday, 17.12.2024 15:45 - 17:15
Pre-requisites Basic knowledge of python programming language and jupyter notebooks is expected.
Veranstaltung findet in Präsenz statt / hat Präsenz-Bestandteile Yes
Hauptunterrichtssprache englisch
Literaturhinweise • A. Geron, Hands-On Machine Lerning with Scikit-Learn, Keras & TensorFlow (2019 by O’Reilly Media Inc.)

Rooms and times

No room preference
Tuesday: 15:45 - 17:15, weekly(2x)
(S448)
Tuesday: 15:45 - 17:15, weekly (1x)
(T2003)
Tuesday, 15.10.2024 14:00 - 15:30

Module assignments

Comment/Description

Machine learning is the science of programming computers so that they can learn from data. Machine learning has become an indispensable tool in research and development. Applications range from spam filters for Emails to voice and image recognition. In this hands-on seminar you will learn the basic principles and how to apply machine learning algorithms for instance using Scikit-learn, Keras and TensorFlow.

The topics for the presentations are from the following subject areas
• Classification tasks in machine learning
• Training of machine learning algorithms
• Decision tree learning
• Ensemble learning and random forests
• Dimensional reduction
• Unsupervised learning techniques
• Multilayer perceptrons and backpropagation
• Deep neural networks

Admission settings

The course is part of admission "Beschränkte Teilnehmendenanzahl: Seminar on Machine Learning".
The following rules apply for the admission:
  • A defined number of seats will be assigned to these courses.
    The seats in the affected courses have been assigned at 15.10.2024 on 14:00. Additional seats may be available via a wait list.