PROTOTYPE PROGRAMME: Machine Learning for Defence

GENERAL INFORMATION

Format: On-site

Language: French / English

EQF level: 6

Hours: 56

Instructors:
PESQUET Baptiste, Bordeaux INP
AIRIMITOAIE Tudor-Bogdan, UBx
ZEMMARI Akka, UBx

Hosting Department:
Unité de formation – Science de l’Ingénieur
(Training Unit – Engineering Science)

Access conditions:
B2 English level
Mathematics and Computer Science: linear algebra, statistics, programming
Python programming
Problem specification and solving
Python

Host institution: University of Bordeaux (France)

PROGRAMME CALENDAR

MODULE 1 (January, 2022)
26/01 8.30 – 10.15 Introduction to Machine Learning
10.30 – 12.00 Conference with a professional of the sector
14.00 – 17.30 Launch of the mini-projects in groups
27/01 8.30 – 10.15 Machine Learning Fundamentals
10.30 – 12.00
14.00 – 17.30 Mini-projects in groups
28/01 8.30 – 10.15 K-NN and linear models
10.30 – 12.00
14.00 – 17.30 Mini-projects in groups
MODULE 2 (February, 2022)
14/02 8.30 – 10.15 Bayes and SVM
10.30 – 12.00 Conference with a professional of the sector
14.00 – 17.30 Mini-projects in groups
15/02 8.30 – 10.15 ANN and CNN (Neural Networks)
10.30 – 12.00
14.00 – 17.30 Mini-projects in groups
MODULE 3 (March, 2022)
16/03 8.30 – 10.15 Unsupervised Learning
10.30 – 12.00 Conference with a professional of the sector
14.00 – 17.30 Mini-projects in groups
17/03 8.30 – 10.15 ML issues and ML Ops
10.30 – 12.00
14.00 – 17.30 Mini-projects in groups
18/03 8.30 – 10.15 Presentation of the mini-projects
10.30 – 12.00
14.00 – 17.30 Closing session

Learning outcomes that will be implemented:

– Machine Learning overview
– Model training & evaluation
– Core Machine Learning algorithms
– Machine Learning issues & MLOps
– Prepare datasets for training
– Build Machine Learning models on various data types
– Use standard Python libraries for Machine Learning
– Understand the benefits and limits of Machine Learning approaches
– Apply Machine Learning wisely, in accordance with ethical rules

Knowledges:
• algorithms
• decision support systems
• mathematical modelling in missions
• model based system engineering
• principles of artificial intelligence
• signal processing
• computer programming (python, c, c++, r, java, matlab, lisp, prolog)
• data mining

Skills:
• analyse big data
• apply deep learning architectures
• demonstrate willingness to learn
• run simulations
• using digital tools for processing sound and images
• utilise machine learning
• solving problems
• address problems critically
• develop data processing applications
• develop software prototype
• identify service requirements
• interact through digital technologies
• use ict systems
• use interface description language
• use online communication tools
• use specific data analysis software

  • Data scientist
  • Data analyst
  • ICT Intelligence Systems designer

IMPORTANT: This prototyped programme is EXCLUSIVE FOR partners of the ASSETs+ consortium and associated stakeholders. If you want to join the ASSETs+ Stakeholders Group and become part of our ecosystem, please, click here.