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Machine Intelligence II (unsupervised methods)
- © NI, TUB
Topics covered
- Probabilities and densities
- Density estimation
- Maximum likelihood
- Principal Component Analysis
- Hebbian learning
- Kernel PCA
- Independent Component Analysis
- Stochastic optimization
- K-means clustering
- Pairwise clustering
- Self-Organizing Maps
General information
- The courses Machine Intelligence I and II can be heard independently.
- Information regarding the tutorials can be found on the ISIS page.
- Lecture and tutorials are held in English, oral exams can be taken in English or in German.
Prerequisites
- Mathematical knowledge: analysis, linear algebra, probability calculus and statistics.
- Basic programming skills, preferably R, Matlab, or Python.
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 & oral exam |
| Diplom-Informatik | Wahlfach: separate modules possible Graded course credit for the tutorials (25%) and oral exam (75%) |
| Schwerpunkt: separate modules possible Oral block exam, assignments not mandatory | |
| Other study programs | Each of the two courses (I or II) can be taken as a separate module (6 ECTS). 60% of all assignments & oral exam |
Supplemental material
- Table of contents
- Bibliography
- Suggested readings
- Proof of the Cramer-Rao Bound
- Efficiency of the maximum Likelihood Estimator
- Covergence Properties of Oja's Rule
- Mercer's Theorem
- Natural Gradient
- Kurtosis Optimization
For further information please consult Klaus Obermayer (lecturer, in charge) or Timm Lochmann (tutor).