• Machine Learning » Introduction » Organization, Literature
• Machine Learning » Introduction » Learning Problems


• Data Mining » Introduction » On Data
• Machine Learning » Machine Learning Basics » Regression
• Machine Learning » Machine Learning Basics » Concept Learning
• Machine Learning » Machine Learning Basics » Measuring Performance


• Machine Learning » Decision Trees » Decision Trees Basics
• Machine Learning » Decision Trees » Impurity Functions
• Machine Learning » Decision Trees » Decision Tree Algorithms
• Machine Learning » Decision Trees » Decision Tree Pruning


• Machine Learning » Statistical Learning » Probability Basics
• Machine Learning » Statistical Learning » Bayes Classification


• Machine Learning » Neuronal Networks » Perceptron Learning
• Machine Learning » Neuronal Networks » Multilayer Perceptron


• Data Mining » Introduction » Data Mining Overview
• Data Mining » Cluster Analysis » Cluster Analysis Basics
• Data Mining » Cluster Analysis » Hierarchical Cluster Analysis
• Data Mining » Cluster Analysis » Iterative Cluster Analysis
• Data Mining » Cluster Analysis » Density-Based Cluster Analysis
• Data Mining » Cluster Analysis » Cluster Evaluation

• Lab Class (ML: I, II) [exercises]
• Lab Class (ML: II) [exercises]
• Lab Class (ML: III) [exercises]
• Lab Class (ML: IV) [exercises]
• Lab Class (ML: VI) [exercises]
• Lab Class (ML: XI) [exercises]

The deadline and the exercises to be submitted are on the exercise sheet.

Lab class dates:

• [2017-10-26] Lab class introduction [slides]
  
• [2017-11-02] Tutorial [slides]
• [2017-11-09] Lab class ML I,II [slides]; Tutorial II [slides]
• [2017-11-23] Lab class ML II [slides]
• [2017-12-07] Lab class ML III [ML:IV starter code]
• [2017-12-21] Lab class ML IV [slides]
• [2018-01-11] Lab: Backpropagation [slides]
• [2018-01-18] Lab class ML VI [slides]
• [2018-02-01] Lab class ML XI

Group work up to 3 people is allowed. Please provide both your name(s) and student number(s) with your solutions. To submit your solutions, send an email to Michael Völske or Wei-Fan Chen with a single PDF file or ZIP archive, and name the file <last name>-<student number>-ml-lab<lab-class-number>.[pdf|zip], e.g. "meier-4711-schulz-1234-ml-lab3.pdf" or "meier-4711-schulz-1234-ml-lab3.zip". If you need to submit source code, include it in the ZIP archive. Thank you!

Machine Learning:

• Christopher M. Bishop. Pattern Recognition and Machine Learning. 2nd edition, Springer 2007.
• Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone. Classification and Regression Trees. CRC Press reprint, 1998.
• Nello Cristianini, John Shawe-Taylor. An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, 2000.
• Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning. 2nd edition, Springer, 2009.
• Tom Mitchell. Machine Learning. 1st edition, McGraw-Hill, 1997.
• Vladimir Vapnik. The Nature of Statistical Learning Theory. 2nd edition, Springer 2000.

Data Mining:

• David Hand, Heikki Mannila, Padhraic Smyth. Principles of Data Mining. Bradford, 2001.
• Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. 1st edition, Addison Wesley, 2005.
• Ian H. Witten, Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. 3rd edition, Morgan Kaufmann, 2011.
• Anil K. Jain. Data Clustering: 50 Years Beyond K-Means. Pattern Recognition Letters, Vol. 31, Issue 8, 2010.

Background:

• Eliezer S. Yudkowski. An Intuitive Explanation of Bayes' Theorem. 2003