Machine Intelligence II (unsupervised methods)

The course will be offered in an online-form in the upcoming summer semester 2021.

Details will be explained in the ISIS course, which will be opened by Mon 12.04.2021 if not sooner. Access to the ISIS course requires a tubIT account.

General information

• The courses Machine Intelligence I and II can be heard independently.
• Information regarding the material, tutorials and the exam can be found on the ISIS page.
• The lecture and tutorials are held in English.
• The course is open to TU students as well as exchange students and visiting students.
• You do not need a formal registration to attend the course. The registration is only relevant for the exam.
• The exam registration procedure is announced in the first lecture. Prior registration/reservation is not possible.

Topics covered

• Principal Component Analysis
• Hebbian learning
• Kernel PCA
• Independent Component Analysis
• Stochastic optimization
• K-means clustering
• Pairwise clustering
• Self-Organizing Maps
• Locally Linear Embedding
• Probability density estimation
• Mixture models & Expectation-Maximization algorithm
• Hidden Markov Models
• Estimation theory

Prerequisites

• Solid mathematical knowledge: analysis, linear algebra, probability calculus and statistics. We emphasize this requirement because the course deals with the theoretical aspects and mathematical formulations of the learning algorithms.
• Basic programming skills, preferably Python, R, Matlab, or Julia. The programming skills are relevant for solving the programming exercises.

Target Audience / Assessment and Grading

For further information please consult Klaus Obermayer (lecturer, in charge) or Youssef Kashef (assistant).