Machine Learning and Pervasive Computing (Winter 2014/2015): Difference between revisions

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== Details ==
== Details ==
{{CourseDetails
{{CourseDetails
|credits=180h, 6 ECTS
|credits=180h, 5 ECTS
|module=M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke
|module=M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke
|lecturer=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoPerson&publishSubDir=personal&keep=y&purge=y&personal.pid=128205 Stephan Sigg]
|lecturer=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&moduleCall=webInfo&publishConfFile=webInfoPerson&publishSubDir=personal&keep=y&purge=y&personal.pid=128205 Stephan Sigg]

Revision as of 08:10, 10 October 2014

Details

Workload/ECTS Credits: 180h, 5 ECTS
Module: M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke
Lecturer: Stephan Sigg
Teaching assistant: --
Time: Wednesdays, 10.15 - 11.45.; Exercise: 16:15 - 17:45 (bi-weekly)
Place: IfI 3.101
UniVZ [1]


Course Overview

The course will address selected topics in Pervasive Computing. This semester the main focus of the lecture will be on Machine learning and activity recognition from sensor-data. In addition, other fields of Pervasive Computing are covered to provide students with a good overview on current advances and research challenges. Depending on the interest of the students, the emphasis on these additional topics may differ.

  • Course topics
    • Introduction to Machine learning
    • Supervised and Unsupervised learning
    • Features and feature extraction
    • Feature subset selection
    • Performance metrics
    • Polynomial curve fitting
    • Support Vector Machines
    • Artificial Neural Network learning
    • Clustering (k-means)
    • Dimensionality reduction
    • Anomaly detection
    • Recommender systems



Schedule

  • Wednesday, October 22nd, 2014, 10.15 - 11.45: Lecture 1
  • Wednesday, October 29th, 2014, 10.15 - 11.45: Lecture 2
  • Wednesday, November 5th, 2014, 10.15 - 11.45: Lecture 3
  • Wednesday, November 5th, 2014, 16.15 - 17.45: Exercise 1
  • Wednesday, November 12th, 2014, 10.15 - 11.45: Lecture 4
  • Wednesday, November 19th, 2014: No lecture
  • Wednesday, November 26th, 2014, 10.15 - 11.45: Lecture 5
  • Wednesday, November 26th, 2014, 16.15 - 17.45: Exercise 2
  • Wednesday, December 3rd, 2014: No lecture
  • Wednesday, December 10th, 2014, 10.15 - 11.45: Lecture 6
  • Wednesday, December 17th, 2014, 10.15 - 11.45: Lecture 7
  • Wednesday, December 17th, 2014, 16.15 - 17.45: Exercise 3
  • Wednesday, January 7th, 2015, 10.15 - 11.45: Lecture 8
  • Wednesday, January 14th, 2015, 10.15 - 11.45: Lecture 9
  • Wednesday, January 14th, 2015, 16.15 - 17.45: Exercise 4
  • Wednesday, January 21st, 2015, 10.15 - 11.45: Lecture 10
  • Wednesday, January 28th, 2015, 10.15 - 11.45: Lecture 11
  • Wednesday, January 28th, 2015, 16.15 - 17.45: Exercise 5
  • Wednesday, February 4th, 2015, 10.15 - 11.45: Lecture 12

Requirements

  • Each participant is required to attend and actively participate in the exercises.
  • Oral examination at the end of the semester

Reading List

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