Machine Learning (SS 2017)

Inhalt

Date	Topic	Literature
20.04.2017	Chapter 1: Introduction What is Machine Learning? Structured vs. unstructured data Nominal, ordinal, and numerical features Supervised vs. unsupervised learning	Chapters 1 in [1,2,4]
27.04.2017	Chapter 2: Regression Ordinary Least Squares Gradient Descent	Chapter 10 in [3]; Chapter 8 in [4]
04.05.2017	Multiple Linear Regression Handling Non-Numerical Features Polynomial Regression Evaluation Fundamentals Regularization	Chapters 6 & 10 in [3]; Chapter 8 in [4]
11.05.2017	Chapter 3: Classification Logistic Regression Evaluating Classifiers	Chapter 5 & 7 in [4]; Chapters 3 & 6 in [3]
18.05.2017	k-Nearest-Neighbors Naïve Bayes	Chapter 2 & 4 in [4]; Chapter 3 in [3]
~~25.05.2017~~	Ascension Day
01.06.2017	Decision Trees Ensemble Learning	Chapter 3 & 7 in [4]; Chapter 3 & 7 in [3]
08.06.2017	Chapter 4: Clustering k-Means Evaluating Clusterings Hierarchical Clustering	Chapter 10 in [4]; Chapter 11 in [3]; Chapter 13 & 14 in [2]
~~15.06.2017~~	Corpus Christi
22.06.2017	Density-based Clustering	Chapter 11 in [3]; Chapter 15 in [2]
29.06.2017	Chapter 5: Next Steps Resources to learn more Q & A

Written exam at the end of the lecture period (90 minutes, non-programmable calculator, three handwritten sheets of DIN-A4 paper)

[1] G. James, D. Witten, T. Hastie, R. Tibshirani: An Introduction to Statistical Learning - with Applications in R, Springer, 2015 [Online]
[2] M. J. Zaki and W. Meira Jr.: Data Mining and Analysis: Fundamental Concepts and Algorithms, Cambridge University Press, 2014 [Online]
[3] S. Raschka: Python Machine Learning, PACKT Publishing, 2015
[4] P. Harrington: Machine Learning in Action, Manning, 2012