Machine Learning and Pervasive Computing (Summer 2015)

Details

Workload/ECTS Credits:	180h, 5 ECTS
Module:	M.Inf.1223: Spezielle fortgeschrittene Aspekte der Computernetzwerke; ITIS: 3.33
Lecturer:	Stephan Sigg
Teaching assistant:	--
Time:	Mondays, 08.15 - 09.45.; Exercise: (bi-weekly)
Place:	IfI 3.101
UniVZ	[1]

Course Overview

The course will give a comprehensive overview on Machine learning with applications in Pervasive Computing.

• Course topics
  • Introduction to Machine learning
  • Features and feature extraction
  • Feature subset selection
  • Performance metrics
  • Rule-based learning
  • Regression approaches
  • High dimensional data
  • Artificial Neural Network learning
  • Probabilistic approaches
  • Topic models
  • nearest neighbour methods
  • Unsupervised learning and Clustering
  • Anomaly detection
  • Recommender systems

Schedule

• Monday, April 13th, 2015, 08.15 - 09.45: Lecture 1
  • Organisation (pdf)
  • Introduction (pdf)
  • Lecture notes (Introduction) (pdf)
• Monday, April 20th, 2015, 08.15 - 09.45: Lecture 2
  • Rule-based (pdf)
• Monday, April 27th, 2015, 08.15 - 09.45: Lecture 3
  • Decision tree (pdf)
  • Projects meeting (Group allocation) (pdf)
• Monday, May 04th, 2015, 08.15 - 09.45: Lecture 4
  • Regression (pdf)
  • Lecture notes (Regression) (pdf)
  • Assignment 01 (pdf)
• Monday, May 11th, 2015, 08.15 - 09.45: No lecture
• Monday, May 18th, 2015, 08.15 - 09.45: Lecture 5
  • Local random search (pdf)
  • Assignment 02 (pdf)
• Monday, May 25th, 2015, 08.15 - 09.45: No lecture
• Monday, June 01st, 2015, 08.15 - 09.45: Lecture 6
  • High dimensional data (pdf)
  • Lecture notes (High dimensional data) (pdf)
  • Assignment 03 (pdf)
• Monday, June 08th, 2015, 08.15 - 09.45: Lecture 7
  • Artificial Neural Networks (pdf)
  • Lecture notes (Artificial Neural Networks) (pdf)
• Monday, June 15th, 2015, 08.15 - 09.45: Lecture 8
  • Instance-based learning (pdf)
  • Assignment 04 (pdf)
• Monday, June 22nd, 2015, 08.15 - 09.45: Lecture 9
  • Probabilistic Graphical Models (pdf)
• Monday, June 29th, 2015, 08.15 - 09.45: Lecture 10
  • Topic Models (pdf)
• Monday, July 06th, 2015, 08.15 - 09.45: Lecture 11
  • Unsupervised Learning (pdf)
  • Clustering and density based clustering (Thach Nguyen) (pdf)
• Monday, July 13th, 2015, 08.15 - 09.45: Lecture 12
  • Anomaly detection, Online learning, Recommender Systems (pdf)

Requirements

• Active participation in the exercises required.

Reading List

• Pervasive and Ubiquitous Computing
  • [Marc Weiser on challenges in Ubicomp]
  • [Recent challenges with new devices and interaction]

• Activity recognition
  • [Activity recognition in Pervasive Computing]