Machine Learning II

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Machine Learning II - Course Details

Delve into the course contents and find out about the faculty members.

Single Course Price:

800.00 EUR (tax exempt)

 

Instructor:

Christoph Kern, PhD (Univeristy of Mannheim)
Trent, D. Buskirk, PhD (Bowling Green State University)

Video lecture:

Christoph Kern, PhD (Univeristy of Mannheim)
Trent, D. Buskirk, PhD (Bowling Green State University)

 

Course Dates

To see all courses in the upcoming term click here.

 

This course is part of the Mannheim Data Science Certificate: Big Data & Machine Learning. Book this course or the entire certificate here!

In order to book the course with alumni conditions, please get in touch with Manon Pfeifer directly.

 

Course Description

Short Course Description
Social scientists and survey researchers are confronted with an increasing number of new data sources such as apps and sensors that often result in (para)data structures that are difficult to handle with traditional modeling methods. At the same time, advances in the field of machine learning (ML) have created an array of flexible methods and tools that can be used to tackle a variety of modeling problems. Against this background, this course discusses advanced ML concepts such as cross validation, class imbalance, Boosting and Stacking as well as key approaches for facilitating model tuning and performing feature selection. In this course we also introduce additional machine learning methods including Support Vector Machines, Extra-Trees and LASSO among others. The course aims to illustrate these concepts, methods and approaches from a social science perspective. Furthermore, the course covers techniques for extracting patterns from unstructured data as well as interpreting and presenting results from machine learning algorithms. Code examples will be provided using the statistical programming language R.

Prerequisites
Topics covered in the course Introduction to Machine Learning and Big Data (ML I), i.e.:

  • Conceptual basics of machine learning (training vs. test data, model evaluation basics)
  • Decision trees with CART
  • Random forests

Familiarity with the statistical programming language R is strongly recommended.

Participants are encouraged to work through one or more R tutorials prior to the first-class meeting. Some resources can be found here:

Course Objectives
By the end of the course, students will…

  • will have a profound understanding of advanced (ensemble) prediction methods
  • have built up a comprehensive ML toolkit to tackle various learning problems
  • know how to (critically) evaluate and interpret results from ''black-box'' models

Course Composition
This is a 4 ECTS course, which runs for 8 weeks. The content of the course is broken down into 8 units:

  1. Intro: Bias-variance trade-off, cross-validation (stratified splits, temporal cv) and model tuning (grid and random search)
  2. Classification: Performance metrics (ROC, PR curves, precision at K) and class imbalance (over- and undersampling, SMOTE)
  3. Ensemble methods I: Bagging and Extra-Trees
  4. Ensemble methods II: Boosting (Adaboost, GBM, XGBoost) and Stacking
  5. Variable selection: Lasso, elastic net and fuzzy/ recursive random forests
  6. Support Vector Machines
  7. Advanced unsupervised learning: Hierarchical clustering and LDA
  8. Interpreting (Variable Importance, PDP, ...) and reporting ML results

Learning and Teaching Methods

In this course, you are responsible for watching video-recorded lectures and reading the required literature for each unit prior to participating in mandatory weekly one-hour online meetings where students have the chance to discuss the materials from a unit with the instructor. In addition, students are encouraged to post questions about the materials covered in the videos and readings of the week in the forum before the meetings.

Grading
Grading will be based on:

  • 4 homework assignments (worth 40% total)
  • 8 online quizzes (worth 40% total)
  • Participation in discussion during the weekly online meetings (worth 20%)

 

ZFU Certification and Online Dispute Resolution

ZFU Certification

The Mannheim Master of Applied Data Science & Measurement program is certified according to the regulations of the ZFU (Staatliche Zentralstelle für Fernunterricht).

 

Online Dispute Resolution

Online dispute resolution according to Art. 14 Sect. 1 ODR-VO: The European Commission provides a platform for online dispute resolution (ODR). You can find more information under http://ec.europa.eu/consumers/odr/

MANNHEIM BUSINESS SCHOOL (MBS)

Located in the heart of the German and European economy, Mannheim Business School (MBS), the umbrella organization for management education at the University of Mannheim, is considered to be one of the leading institutions of its kind in Germany and is continuously ranked as Germany’s #1.

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