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Machine learning course with R

40 hour(s)

2 x 2 hours per week or dates and time to be determined with participants.

Objectives

This course is technical in nature. It makes use of coding in R and covers the application of machine learning in business.

You will learn:

The fundamental of machine learning techniques that can be applied in a wide range of applications (e.g., finance, banking, real state, transportation/mobility, environmental monitoring, and agriculture, to cite a few).

Content

Section 1: Statistics with R

  • Introduction to Statistics with R
  • Probability
  • Random variables
  • Statistical Inference
  • Statistical models
  • Regression
  • Linear Models
  • Association is not causation

Section 2: Data Wrangling

  • Introduction to Data Wrangling
  • Reshaping data
  • Joining tables
  • Web Scraping
  • String Processing
  • Parsing Dates and Times
  • Text mining

Section 3: Machine Learning

  • Introduction to Machine Learning
  • Smoothing
  • Cross validation
  • The caret package
  • Examples of algorithms
  • Machine learning in practice
  • Large datasets
  • Clustering and classification

Section 4: Productivity tools

  • Introduction to productivity tools
  • Organizing with Unix
  • Git and GitHub
  • Reproducible projects with RStudio and R markdown

Target group

Who is the course aimed at?

  • Students and Recent Graduates
  • Early and Mid-Career Professionals
  • Marketing and Project Management Professionals

 
Prerequisites

R studio software is recommended but not mandatory to enable programming directly in a software-based interface.

Assessment

Certficate, diploma

In order to earn a Certificate of Completion, participants must thoughtfully complete all 4 modules.

Additional information

About the Professor

Dr Hichem Omrani (Research Scientist, full ADR / Habilitation-HDR)
Dr. Hichem Omrani is currently a research scientist (R3) at the Luxembourg Institute of Socio-Economic Research (LISER). his research group is hosted at the Urban development and Mobility department. He received his Ph.D. in Computer Science in 2007, from the Technology University of Compiegne (UTC-France). Dr. Omrani is also an Adjunct Associate Professor at the University of Concordia (Canada). He conducts research in the framework of several competitive projects supported mainly by the FNR. His research focuses on data science, applied statistics, machine learning, complex system modelling applied in a wide range of applications (environment, mobility, policy evaluation, and individual exposure to the environments, and public heath). He supervised several master/PhD students, young researchers and act as a reviewer for several international journals. During his lastest scientific leave (2016-2017), he served as Senior Visiting Researcher at Purdue University (USA), working jointly with Dr. Bryan Pijanowski, an internationally recognized specialist in the field of environmental science and soundscape. Dr. Omrani is (co-) author of 45+ scientific articles on peer-reviewed international journals, 100+ papers in conferences proceedings, and 2 book chapters in volumes with ISBN. His publications have been cited, so far, 1500 times (source: Google scholar). My Google scholar H-index is 17 (i10-index: 26). To date, he has attracted around €4.25m in grants mainly from the EU, FNR and industry.

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Responsibility for the content of this training description lies solely with its author, the training provider Centre Avicenne Luxembourg.

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Characteristics
Level Avancé
Organisation Formation inter et intra-entreprise

Other details of the course can be arranged with participants.

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