Machine & Deep Learning

Practical Introduction
3th to 7th of September

09:15 - 17:30
University of Neuchâtel

This 5 days course aims to provide basic understanding of the most used machine learning and deep learning algorithms.

This 5 days course aims to provide basic understanding of the most used machine learning and deep learning algorithms. It is an intensive course that, without going into too much mathematical details, provides the necessary foundations to start testing, working with and evaluating those algorithms. Knowing how to manipulate these algorithms and their potential forms an important building block in the digital literacy required to prepare and live in Society 4.0.

Topics covered

  • regression (linear, logistic) ;
  • classification (K-NN);
  • dimensionality reduction (PCA) ;
  • support vector machines ;
  • clustering (K-means) ;
  • decision trees ;
  • Bayesian learning;
  • neural networks ;
  • deep neural networks (CNN, RNN and LSTM) ;
  • data cleaning, models’ evaluation and features selection.


By the end of the course, participants should be able to

  • Categorize the different algorithms and their use case ;
  • Efficiently select algorithms’ features and evaluate algorithms’ performance ;
  • Explain and use linear and logistic regression methods ;
  • Explain and use classification and clustering algorithms ;
  • Explain and use dimensionality reduction, decision trees and Bayesian learning;
  • Explain and use artificial neural networks and deep learning methods.

Target Audience

  • Techniciens in Big Data and Business Intelligence ;
  • Business analysts ;
  • Big Data and Business Intelligence Project managers ;
  • Students from universities and schools of applied sciences at master level or higher.

​The course is nevertheless open to other profiles of professionals and academics interested in discovering machine learning and deep learning and how they can be applied in their domain.

Admission Conditions

Given the density of the course no introduction to programming nor to mathematics can be provided. Participants are expected to bring their personal computers during the course with full administrative rights, be familiar with the basic use of R and Python and have basic familiarity with linear algebra and statistics.

Specific details on the requirements can be found in the additional documents. A two hours R and Python course can be provided on Friday afternoon before the beginning of the course on Monday. Mention your interest to follow this course while registering.


Upon successful completion of the course, the participants will be awarded with a Certificate of Completion issued by the Faculty of Economics and Business of the University of Neuchâtel.  


19th of August 2018


CHF 1000.- / participant
including lunch and coffee breaks

Information and Registration

Eliane Maalouf : eliane.maalouf@unine.ch

Tel: 032 718 13 29

 PhD candidate - Teaching Assistant

Information Management Institute

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