Neural Information Processing

Machine Intelligence 2 (unsupervised methods)

SoSe23

  • 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

ProgramForm of Assessment
MSc in Computational NeuroscienceThe two courses (Machine Intelligence I and II) form a single module (12 ECTS).
assignments & oral exam
MSc in Computer ScienceEach 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)

Course Information (SS 22)

CreditsTimeLecturerRoomLanguage
2 SWS Lecture
0434 L 867
Fri 8-10Prof. Dr. Klaus ObermayerMA 004English
2 SWS TutorialThu 12-14Ronja StrömsdörferMA 043English
2 SWS TutorialFri 14-16Ronja StrömsdörferMAR 4064 (Comp. Neuro. only)English