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Machine Intelligence II (unsupervised methods)
SoSe22
- Details regarding the weekly format will be explained in the ISIS course
- The ISIS course will be opened shortly before the lecture period starts. Access to the ISIS course requires a tubIT account.
- The lecture and tutorial will be offered in-person (dt. in Präsenz).
- 1 lecture and 1 tutorial every week. No need to attend more than one tutorial during the same week.
- We don't take attendance.
- Access to recorded video material from earlier years will be made available in case you missed a lecture.
- The exam(s) will take place on campus (dt. Präsenzklausur). You will need to be in Berlin to take part in the exam.
General information
- The courses Machine Intelligence I and II can be heard independently.
- Machine Intelligence II is offered in the summer while Machine Intelligence I is offered in the winter.
- Information regarding the material, tutorials and the exam can be found on the ISIS page. See the link to our ISIS course on the panel to the right. ⟶
- The lecture and tutorials are held in English.
- The course is open to TU students as well as exchange students and visiting students. Non-TU students should apply for a Neben-/Gasthörschaft to gain access to the course material.
- You do not need a formal registration to attend the course. You can self-enroll to the ISIS course as soon as its available. The registration is only relevant for the exam in order to earn ECTS points.
- The exam registration procedure will be explained in the ISIS course. Prior registration/reservation is not possible and not necessary.
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 Klaus Obermayer (lecturer, Modulverantworthlicher/in-charge) or Youssef Kashef (assistant).
Zusatzinformationen / Extras
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Lecture
Machine Intelligence II0434 L 867
: Klaus Obermayer
:
22.04.2022
08:15 - 09:45
: H 2013
ISIS
:
The ISIS course page will be made available shortly before the lecture period starts.