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Machine Intelligence II (unsupervised methods)

Lupe

We are still working on ways to offer this course in an online-form in the upcoming summer semester. We will confirm if the course will take place here before the semester starts. 

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.
  • No formal registration is required to attend the course or is even possible. 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

Program
Form of Assessment
MSc in Computational Neuroscience
The two courses (Machine Intelligence I and II) form a single module (12 ECTS).

assignments & oral exam
MSc in Computer Science
Each of the two courses (Machine Intelligence I or II) can be taken as a separate module (6 ECTS).

written exam (no assignments during the course)
Other study programs (e.g., mathematics, natural, and engineering sciences)
Each of the two courses (Machine Intelligence I or II) can be taken as a separate module (6 ECTS).

written exam (no assignments during the course)

 

For further information please consult (lecturer, in charge) or (assistant).

Zusatzinformationen / Extras

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Lecture

Machine Intelligence II
0434 L 867
Lecture

Lecturer: Klaus Obermayer

Period:
from 17.04.2020

Th 08:15 - 09:45 o'clock

Location: MA 005

ISIS

Tutorials

Lecturer: Youssef Kashef

Period:
from 21.04.2020

Th 16:15 - 17:45 o'clock

Location: H 1012



ISIS

Hint:
tutorial Fri: computational neuroscience only; tutorial Tue: all other study programs