This lecture/module is part of the international track and delivered in English.
The lecture discusses basic notions of machine learning, starting from supervised learning
for regression and classification. It further introduces standard neural networks models, the perceptron and the multi-layer perceptron. Tunrning to unsupervised learning, several algorithms for vector quantization are introduced, Hebb-learning, and Self-Organizing Maps.
Note that the lecture is organized with the help of unimoodle. You can self-subscribe by
logging on via unimoodle.uni-bielefeld.de and the password NN+ML2015. All course material, lecture notes, and exercises will be published through moodle only.
Algorithmen und Datenstrukturen, Vertiefung Mathematik. NOTE that machine learning is an applied math topic - make sure that you recall the necessary basic math.
Part of the lecture will lean on C. Bishop, Pattern Recognition and Machine Learning, Springer 2006, in particular Chap. 1, Chap. 3.1-3.3 (linear models für regression), Chap 4.1. /4.2. (für classification) , Chap 5. (feedforward neural networks). Furthermore, lecture notes (in English) are available.
Frequency | Weekday | Time | Format / Place | Period | |
---|---|---|---|---|---|
weekly | Fr | 10-12 | H11 | 19.10.2015-12.02.2016
not on: 12/25/15 / 1/1/16 |
Module | Course | Requirements | |
---|---|---|---|
39-Inf-NN_ver1 Grundlagen Neuronaler Netze | Neuronale Netze und Lernen I | Graded examination
|
Student information |
The binding module descriptions contain further information, including specifications on the "types of assignments" students need to complete. In cases where a module description mentions more than one kind of assignment, the respective member of the teaching staff will decide which task(s) they assign the students.
Degree programme/academic programme | Validity | Variant | Subdivision | Status | Semester | LP | |
---|---|---|---|---|---|---|---|
Bioinformatik und Genomforschung / Bachelor | (Enrollment until SoSe 2011) | Grundlagen Neuronaler Netze | Wahlpflicht | 5. | aktive Teilnahme | ||
Informatik / Bachelor | (Enrollment until SoSe 2011) | Nebenfach | Grundlagen Neuronaler Netze; Neuronale Netze und Lernen | Wahlpflicht | 5. | aktive Teilnahme | |
Kognitive Informatik / Bachelor | (Enrollment until SoSe 2011) | Neuronale Netze und Lernen | Pflicht | 5. | aktive Teilnahme | ||
Molekulare Biotechnologie / Bachelor | (Enrollment until SoSe 2011) | Grundlagen Neuronaler Netze | Wahlpflicht | 5. | aktive Teilnahme | ||
Naturwissenschaftliche Informatik / Bachelor | (Enrollment until SoSe 2011) | Neuronale Netze und Lernen | Wahlpflicht | 5. | aktive Teilnahme | ||
Naturwissenschaftliche Informatik / Diplom | (Enrollment until SoSe 2004) | Robotik; Physik; Biologie; NNet; ME | Teilleistung mündliche Prüfung möglich HS | ||||
Studieren ab 50 |
The lecture achieves 2 CP, the corresponding exercises 2 CP, and an oral exam, which is necessary to complete the modul, adds 1 CP. The detailed schedule for the exercises is
published through moodle.
The current module will be continued in the SS 2016 with the 5 CP module "Grundlagen Maschinelles Lernen" (Introduction to machine learning). Both modules are formally independent, but the SS 2016 will partially rely on the material presented now.