623.613 (17S) Applied Machine Learning

Sommersemester 2017

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First course session
22.05.2017 09:00 - 12:00 E 1.37 Off Campus
... no further dates known

Overview

Lecturer
Course title german Applied Machine Learning
Type Lecture - Course (continuous assessment course )
Hours per Week 2.0
ECTS credits 4.0
Registrations 11 (25 max.)
Organisational unit
Language of instruction English
possible language(s) of the assessment German
Course begins on 22.05.2017
eLearning Go to Moodle course

Time and place

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Course Information

Intended learning outcomes

The course provides a practical introduction into machine learning methods applied in computer science. 

Note: The course appointments are blocked and are planned for May.

Teaching methodology including the use of eLearning tools


The course is build on top of the "Uncertain Knowledge" (621.065) and assumes the knowledge of the presented material.

In addition, it is recommended to visit the course "Selected Topics in Artificial Intelligence" (623.131) for an in-depth review of data analysis methods as well as learning of bayesian networks, rule-sets and decision trees.

Knowledge of R and/or Python is a plus.

Course content

  • Introduction to machine learning
  • Supervised learning: classification and regression
  • Unsupervised learning: transformation of data and clustering
  • Validation of models
  • Overview of reinforcement learning

Literature

Please also consider visiting the course "Selected Topics in Knowlegde and Data Engineering: Data Mining" (623.253) given by Prof. Morzy for an in-depth review of data acquisition and manipulation methods.


Beginners:

  • James, G., Witten, D., & Hastie, T. (2014). An Introduction to Statistical Learning: With Applications in R. Springer
  • Raschka, S. (2015). Python machine learning. Packt Publishing Ltd.

Classics:

  • Mitchell, T. (1997) Machine Learning. McGraw Hill.
  • Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
  • Friedman, J., Hastie, T., & Tibshirani, R. (2009). The elements of statistical learning.  2nd edition, Springer.


Examination information

Im Fall von online durchgeführten Prüfungen sind die Standards zu beachten, die die technischen Geräte der Studierenden erfüllen müssen, um an diesen Prüfungen teilnehmen zu können.

Grading scheme

Grade / Grade grading scheme

Position in the curriculum

  • Master's degree programme Applied Informatics (SKZ: 911, Version: 13W.1)
    • Subject: Business Information Systems (Compulsory elective)
      • Topics in Knowledge and Data Engineering for Business Information Systems ( 2.0h VK / 4.0 ECTS)
        • 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)
  • Master's degree programme Applied Informatics (SKZ: 911, Version: 13W.1)
    • Subject: Knowledge and Data Engineering (Compulsory elective)
      • Knowledge Representation and Reasoning for Games ( 2.0h VK / 4.0 ECTS)
        • 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)
  • Masterstudium Informatik (SKZ: 921, Version: 09W.1)
    • Subject: Intelligent Information Systems in Production, Operation and Management (POM) (Compulsory subject)
      • Weitere Lehrveranstaltungen aus dem Spezialisierungsfach ( 4.0h XX / 8.0 ECTS)
        • 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)
  • Master's degree programme Informatics (SKZ: 921, Version: 03W.1)
    • Subject: Intelligent Information Systems in Production, Operation and Management (POM) (Compulsory subject)
      • Weitere Lehrveranstaltungen aus dem Spezialisierungsfach ( 4.0h XX / 8.0 ECTS)
        • 623.613 Applied Machine Learning (2.0h VC / 4.0 ECTS)

Equivalent courses for counting the examination attempts

This course is not assigned to a sequence of equivalent courses