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252-0220-00L 8 Credits BSC , MSC , NDS , WBZ D-HEST , D-MAVT , D-BAUG , D-ARCH , D-INFK , D-MATH , D-PHYS , D-ITET , D-BSSE , D-ERDW , D-GESS
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Introduction to Machine Learning

Lecturers & Examiners: Prof. Dr. Andreas Krause, Prof. Dr. Fan Yang
Preference is given to students in programmes in which the course is being offered. All other students will be waitlisted. Please do not contact the lecturers for any questions in this regard. If necessary, please contact
VVZ CR 3.83

Last Updated: 2026-06-01 11:33:04

Abstract

The course introduces the foundations of learning and making predictions based on data.

Objective

The course will introduce the foundations of learning and making predictions from data. We will study basic concepts such as trading goodness of fit and model complexitiy. We will discuss important machine learning algorithms used in practice, and provide hands-on experience in a course project.

Content

- Linear regression (overfitting, cross-validation/bootstrap, model selection, regularization, [stochastic] gradient descent) - Linear classification: Logistic regression (feature selection, sparsity, multi-class) - Kernels and the kernel trick (Properties of kernels; applications to linear and logistic regression); k-nearest neighbor - Neural networks (backpropagation, regularization, convolutional neural networks) - Unsupervised learning (k-means, PCA, neural network autoencoders) - The statistical perspective (regularization as prior; loss as likelihood; learning as MAP inference) - Statistical decision theory (decision making based on statistical models and utility functions) - Discriminative vs. generative modeling (benefits and challenges in modeling joint vy. conditional distributions) - Bayes' classifiers (Naive Bayes, Gaussian Bayes; MLE) - Bayesian approaches to unsupervised learning (Gaussian mixtures, EM)

Resources

Learning Materials (Links)

General Information

Language
English
Levels
BSC , MSC , NDS , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Two A4-pages (i.e. one A4-sheet of paper), either handwritten or 11 point minimum font size.Simple non-programmable calculator
100% of the final grade is determined by the session examination. As a compulsory continuous performance assessment task, the project component of the course must be passed on it's own to take the exam.The practical projects are an integral part (60 hours of work, 2 credits) of the course.The project component is assessed on a pass/fail basis. Participation is mandatory.Failing the project results in a failing grade for the overall examination of Introduction to Machine Learning (252-0220-00L).Students who do not pass the project component are required to de-register from the exam and will otherwise be treated as a no show.Due to the number of registered students, the exam may be paper-based and will most likely take place on a Saturday.

Registration & Places

Max Places
800
Signup End
09.03.2025
Priority: Registration for the course unit is until 02.03.2025 only possible for the primary target group

Course Components

Type Title Time & Place Hours
lecture Introduction to Machine Learning
Findet im ETA F 5 mit Videoübertragung ins ETF E 1 statt
  • Tue 14:15-16:00 (ETA F 5)
  • Tue 14:15-16:00 (ETF E 1)
  • Wed 14:15-16:00 (ETA F 5)
  • Wed 14:15-16:00 (ETF E 1)
4 h weekly
exercise Introduction to Machine Learning
Findet im ETA F5 mit Videoübertragung ins ETF E1 statt.
  • Fri 14:15-16:00 (ETA F 5)
  • Fri 14:15-16:00 (ETF E 1)
2 h weekly
independent project Introduction to Machine Learning
No presence required.
No time listed 1 h weekly

Offered In