News

• Chance to look at the corrections of the exam on Wednesday, March 18th 2015 in Room B-314.
• No tutorial on Thursday, January 8th 2015.
• Exam date and location: The exam will be held in written form on Tuesday, 3rd of March 2015 from 10:00 (s.t.) to 12:00 in room E-011.
• Change of rooms: from the 13th of November on, lecture and tutorial on Thursdays will be held in room E-412.
• Lecture on Tuesdays starts at 12:00 (s.t.)

Literature

• Introduction to Data Mining. Tan, Steinbach, Kumar. Addison-Wesley, 2006.
• Web Data Mining. Liu. Springer, 2007.
• Data Mining: Practical Machine Learning Tools and Techniques. Witten, Frank, Hall. Morgan Kaufmann, 2011.
• Data Mining - Slides by Michael Möhring (a practical introduction to DM)

Lecture Material

Slides and additional material will be provided along with the progress of the lecture. We will try to publish the material before the lecture.

Lecture

• Lecture / Organisational Issues (PDF) (Powerpoint)
• Introduction (PDF) (Powerpoint)
• Data (PDF) (Powerpoint)
• PCA & SVD (PDF) (Powerpoint)
• Association Rules - Task Definition (PDF) (Powerpoint)
• Association Rules - Apriori (PDF) (Powerpoint)
• Association Rules - Evaluation (PDF) (Powerpoint)
• Association Rules - FP Growth (PDF) (Powerpoint)
• Association Rules - Multiple Support Levels (PDF) (Powerpoint)
• Association Rules - Extensions and (non-standard) Applications (PDF) (Powerpoint)
• Classification - Task Definition and Evaluation (PDF) (Powerpoint)
• Classification - Rule Based (PDF) (Powerpoint)
• Classification - Decision Trees (PDF) (Powerpoint)
• Classification - Naive Bayes (PDF) (Powerpoint)
• Classification - Instance Based (PDF) (Powerpoint)
• Classification - Regression (PDF) (Powerpoint)
• Classification - Artificial Neural Networks (PDF) (Powerpoint)
• Classification - SVMs (PDF) (Powerpoint)
• Clustering - Task Definition and Evaluation (PDF) (Powerpoint)
• Clustering - Single Pass (PDF) (Powerpoint)
• Clustering - K-Means (PDF) (Powerpoint)
• Clustering - Expectation Maximization (PDF) (Powerpoint)
• Clustering - Other approaches (PDF) (Powerpoint)
• Hidden Markov Models (PDF) (Powerpoint)

Tutorials

• 1) Introduction, Measurement Scales, CRISP, Assoc. Rules, Octave
• 2) Entropy, Kullback-Leibler divergence, Information gain
• 3) Maximum-Likelihood-Estimation
• 4) Parameter Estimation for Linear Regression
• 5) Rapid Miner, Expectation-Maximisation, K-Means

Exercises

• Exercise 1 (15.11., updated 17.11.)
• Exercise 2 (29.11.)
• Exercise 3 (16.12.)
• Exercise 4 (26.01.)
• Exercise 5 (03.02.)

Old Exams

• MLaDM exam from Winterterm 2013/14 (PDF)

The lecture material for Machine Learning and Data Mining by Thomas Gottron is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License