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Machine Intelligence II (unsupervised methods)

Lupe



General information

  • The courses Machine Intelligence I and II can be heard independently.
  • Information regarding the tutorials and the exams can be found on the ISIS page.
  • Lecture and tutorials are held in English.
  • The course is limited to 80 participants, the registration procedure is announced in the first lecture.



Topics covered

  • Principal Component Analysis
  • Hebbian learning
  • Kernel PCA
  • Independent Component Analysis
  • Stochastic optimization
  • K-means clustering
  • Pairwise clustering
  • Embeddings
  • Probability density estimation
  • Mixture models & Expectation-Maximization algorithm
  • Estimation theory



Prerequisites

  • Mathematical knowledge: analysis, linear algebra, probability calculus and statistics.
  • Basic programming skills, preferably Python, R, Matlab, or Julia.



Target Audience / Assessment and Grading

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

60% of all assignments & oral exam
MSc in Computer Science
Each of the two courses (I or II) can be taken as a separate module (6 ECTS).

60% of all assignments & written exam
Other study programs (e.g., mathematics, natural, and engineering sciences)
Each of the two courses (I or II) can be taken as a separate module (6 ECTS).

60% of all assignments & written exam

 

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

Zusatzinformationen / Extras

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Auxiliary Functions

Lecture

Machine Intelligence II
0434 L 867
Lecture

Lecturer: Klaus Obermayer

Period:
from 20.04.2017

Th 10:15 - 11:45 o'clock

Location: MA 043

ISIS

Tutorials

Lecturer: Moritz Augustin

Period:
from 27.04.2017

Th 12:15 - 15:45 o'clock

Location: MAR 4.063



ISIS