View source for Machine Learning and Pervasive Computing (Winter 2014/2015)

== Details ==
{{CourseDetails
|credits=180h, 5 ECTS
|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]
|ta= --
|time=Wednesdays, 10.15 - 11.45.; Exercise: 16:15 - 17:45 (bi-weekly)
|place=IfI 3.101
|univz=[https://univz.uni-goettingen.de/qisserver/rds?state=verpublish&status=init&vmfile=no&publishid=146615&moduleCall=webInfo&publishConfFile=webInfo&publishSubDir=veranstaltung]
}}

==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
** [[Media:MLPC_00.pdf | Organisation (pdf)]]
** [[Media:MLPC_01.pdf | Introduction (pdf)]]
* Wednesday, '''October 29th, 2014, 10.15 - 11.45''': Lecture 2
** [[Media:MLPC_02.pdf | Classification and Evaluation (pdf)]]
* Wednesday, '''November 5th, 2014, 10.15 - 11.45''': Lecture 3
** [[Media:MLPC_03.pdf | Simple supervised learning (pdf)]]
* Wednesday, '''November 5th, 2014, 16.15 - 17.45''': Exercise 1
** [[Media:MLPC_Assignment_01.pdf | Assignment 1 (pdf)]]
* Wednesday, '''November 12th, 2014, 10.15 - 11.45''': Lecture 4
** [[Media:MLPC_04.pdf | Random search (pdf)]]
* Wednesday, '''November 19th, 2014''': No lecture
* Wednesday, '''November 26th, 2014, 10.15 - 11.45''': Lecture 5
** [[Media:MLPC_05.pdf | Bayesian networks and Naive Bayes (pdf)]]
* Wednesday, '''November 26th, 2014, 16.15 - 17.45''': Exercise 2
** [[Media:MLPC_Assignment_02.pdf | Assignment 2 (pdf)]]
* Wednesday, '''December 3rd, 2014''': No lecture
* Wednesday, '''December 10th, 2014, 10.15 - 11.45''': Lecture 6  
** [[Media:MLPC_06.pdf | Decision Tree (pdf)]]
* Wednesday, '''December 17th, 2014, 10.15 - 11.45''': Lecture 7
** [[Media:MLPC_07.pdf | K-nearest neighbour (pdf)]]
* Wednesday, '''December 17th, 2014, 16.15 - 17.45''': Exercise 3
* Wednesday, '''January 7th, 2015, 10.15 - 11.45''': Lecture 8
** [[Media:MLPC_08.pdf | Support Vector Machines (pdf)]]
* Wednesday, '''January 14th, 2015, 10.15 - 11.45''': Lecture 9
** [[Media:MLPC_09.pdf | Artificial neural networks and self organizing maps (pdf)]]
* Wednesday, '''January 14th, 2015, 16.15 - 17.45''': Exercise 4
* Wednesday, '''January 21st, 2015, 10.15 - 11.45''': Lecture 10
** [[Media:MLPC_10.pdf | Hidden markov models and conditional random fields (pdf)]]
* Wednesday, '''January 28th, 2015, 10.15 - 11.45''': Lecture 11
** [[Media:MLPC_11.pdf | Dimensionality reduction and unsupervised learning (pdf)]]
* Wednesday, '''January 28th, 2015, 16.15 - 17.45''': Exercise 5
* Wednesday, '''February 4th, 2015, 10.15 - 11.45''': Lecture 12
** [[Media:MLPC_12.pdf | Anomaly detection, online learning and recommender systems (pdf)]]

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

[[Category:Courses]]

==Reading List==
* Pervasive and Ubiquitous Computing
** [[http://dl.acm.org/citation.cfm?id=159617 Marc Weiser on challenges in Ubicomp]]
** [[http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6095491 Recent challenges with new devices and interaction]]

* Activity recognition
** [[http://www.pervasive.jku.at/Teaching/_2012SS/EmbeddedSystems/Uebungen/UE52/2004_Activity%20Recognition%20from%20User-Annotated%20Acceleration%20Data_Intille.pdf Activity recognition in Pervasive Computing]]