Learning Machine Learning with Apache Mahout #

Once in a while I get questions like Where to start learning more on machine learning. Other than the official sources I think there is quite good coverage also in the Mahout community: Since it was founded several presentations have been given that give an overview of Apache Mahout, introduce special features or even go into more details on particular implementations. Below is an attempt to create a collection of talks given so far without any claim to contain links to all videos or lectures. Feel free to add your favourite in the comments section. In addition I linked to some online courses with further material to get you started.

When looking for books of course check out Mahout in Action. Also Taming Text and the data mining book that comes with weka are good starting points for practitioners.

Introductory, overview videos

• Grant Ingersoll: Mahout @ SF Bay Area Mahout meetup
  
• Apache Mahout @ Apache Con Vancouver
  
• Frank Scholten: Configuring Mahout Clustering Jobs
  
• Frank Scholten: Introduction to collaborative filtering using Mahout
  
• Frank Scholten: Clustering @ DataDevRoom FOSDEM (starts at minute 18)
  
• Apache Mahout @ Devoxx Antwerp
  
• Apache Mahout @ Codebits Lisbon
  
• Apache Con US 2009 Oakland
  

Technical details

• Ted Dunning and Ellen Friedman explain logistic regression
  
• Ted Dunning: Mahout @ SF Bay Area Mahout meetup
  
• Apache Mahout for clinical research
  
• Ted Dunning: Mahout @ LA-HUG
  
• Sebastian Schelter: Scaling Apache Mahout recommendations
  
• Max Heimel: Adding HMM support to Apache Mahout
  
• Sean Owen: Simple Co-Occurrence based recommendation on Hadoop
  
• Ted Dunning: Building intelligent search applciations
  

Further course material

• ML course 2011/ Stanford
  
• Intelligente Datenanalyse mit Matlab/ Potsdam
  
• Linear Algebra/ MIT
  
• Videolectures.net … forgot about that one in the original post, sorry.