University training in Machine Learning
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Short presentation

An introduction to the basic concepts for any novice interested in machine learning processes.

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Call to actions

  • Programme title
    University training in Machine Learning
  • Programme mnemonic
    FC-852
  • Programme organised by
    • Centre de formation continue en Technologies et en Sciences
    • Faculty of Sciences
  • Title type
    formation continue
  • Open to returning students
    yes
  • Schedule type
    Daytime
  • Languages of instruction
    english / french
  • Programme duration
    medium (6 to 15 days)
  • Category / Topic
    Sciences and technics - Sciences

Presentation

Details

General information

Title type

formation continue

Programme duration

medium (6 to 15 days)

Learning language(s)

english / french

Schedule type

Daytime

Category(ies) - Topic(s)

Sciences and technics - Sciences

Organising faculty(s) and university(ies) Open to returning students

yes

Prices

Full fee : 950€
Reduced fee (student/unemployed) : 650€
 

Formation continue

Academic teacher

Olivier CAELEN

Contacts

Saturday September 2nd, 2023 : 1st class

Presentation

The objective of the training is to offer keys of understanding to people new to the field of machine learning. The training allows to demystify the tools used by data scientists, to manipulate simple models on real data as well as to provide a solid basis for students wishing to pursue further studies in the field.

The course is composed of two learning methods: theoretical sessions with PowerPoint and programming exercises with Python.

This training will aim to introduce the basic concepts of machine learning to a public totally new to the subject. We will have both a theoretical part with slides and a practical part where we will use the python programming language for the exercises. In the theoretical part, we will introduce concepts such as supervised, unsupervised and reinforcement learning.  Some examples of classical algorithms will be presented such as tree-based models, naive Bayesian classification or neural networks. Without trying to formalize too much, the theoretical aspects of machine learning will be presented both intuitively and with the help of a mathematical formalism.  For the programming part, the basic Python packages used in machine learning will be presented, but participants will be assumed to already have a good command of programming (ideally Python). At the end of the course, the objective is to have demystified the tools used by data scientists, to have manipulated simple models on real data, and to have provided a solid foundation for students wishing to pursue their studies in the field.

Daytime

A total of 6 courses of 4 hours on Saturday morning starting Saturday, September 2, 2023.
From 9h-13h 
 

Calendar & registration

Prerequisites

Admissions are based on a file. Candidates must present a certificate of completion of a first cycle degree, and preferably also a certificate of completion of a second cycle degree. Candidates holding other degrees may apply for a VAE and will have their applications examined on a case-by-case basis by the scientific committee.

Participants are expected to have some experience with programming and if possible in Python. A passive knowledge of English is essential. Experience with programming and an open mind for mathematics are recommended.

Target audience

Person wishing to learn about data science but already having a minimum of programming knowledge.

Calendar & registration

Need help to register? 

Contact us : techsci@ulb.be

Programme

Introduction


An introduction to the basic concepts for any novice interested in machine learning processes. The objective is to demystify the tools used by data scientists and to manipulate simple models on real data. It is a basis for further studies in this field.

For all novices, interested in learning the basic techniques of machine learning who wish to familiarize themselves with simple data manipulation models and demystify the tools used by data scientists.

Program

  • Course 1 : introduction to Machine learning from scratch 
  • Course 2 & 3 :  Machine learning with Python
  • Course 4 : The basics of deep learning 
  • Course 5 : Automatic learning for image and text analysis
  • Course 6 : Data visualization and unsupervised learning 
  • Course 7 : End evaluation (to take in French or English)