Name: AITEC'26 Free Labs
Start: 2026-03-26T09:00
End: 2026-03-28T17:00
Location: Rosa Beach Hotel

Free Lab Programs

AITEC'26 Free Training Sessions

Enhance your skills with our three intensive training programs designed for researchers, engineers, and students. All trainings are completely free for AITEC'26 attendees.

Duration: 2 Days Each

Cost: Free for AITEC'26 Attendees

Location: Rosa Beach Hotel, Monastir

AI Research

AI for Scientific Research

Master AI fundamentals and professional tools for scientific research with hands-on machine learning projects.

AI, ML & Deep Learning Fundamentals

Professional Tools (Anaconda, Jupyter, Scikit-learn)

Advanced Data Processing & Visualization

ML Algorithms & Model Evaluation

Real-world Scientific Projects

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Embedded Systems

Machine Learning Embedded

Learn to implement AI on embedded systems with Raspberry Pi, TensorFlow, and real-time computer vision.

Edge AI vs Cloud AI Concepts

Raspberry Pi Hardware Integration

Neural Networks & CNN Implementation

TensorFlow on Embedded Systems

Object Recognition & Transfer Learning

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Spring Boot

Spring Boot REST API: Best Practices & Security with Keycloak

Build production-ready REST APIs with best practices and secure them with Keycloak in this 100% practical lab session.

REST API Design & Best Practices

DTOs, Validation & Error Handling

Security with Keycloak (OIDC, JWT)

Role-Based Access Control (RBAC)

Production-Ready API Development

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Free Lab 1

AI for Scientific Research

Led by Dr. Radhouane LAAJIMI

View Instructor Profile

Day One (4 hours)

Fundamentals & Professional Tools

Objective 1: Understanding AI Basics and Mastering Essential Tools

General Introduction

Definitions: AI, Machine Learning, Deep Learning
Scientific and industrial application domains
Types of learning (supervised, unsupervised, reinforcement)

Installation and Tool Setup

Anaconda (environment management)
Jupyter Notebook (interactive workflow)
Scikit-learn (quick overview)

Objective 2: Explore, Transform and Visualize Scientific Data

Advanced Pandas

Data cleaning, handling missing values and duplicates
Joins, aggregations, advanced filtering

Data Preprocessing

Normalization and simple feature engineering
Practical Workshop: preparing raw datasets (e.g., country.csv, uforeports.csv)

Advanced Visualization with Matplotlib

Histograms, scatter plots, boxplots
Correlation visualization (heatmap, matrices)
Formatting and customizing graphics
Practical Workshop: create a mini visual report on a scientific dataset

Day Two (4 hours)

Machine Learning Algorithms

Objective 1: Discover and Apply Main Supervised Algorithms

ML Project Steps (CRISP-DM in Detail)

Data Preparation → Modeling → Validation → Evaluation

Supervised Algorithms

Linear and logistic regression
Classification (decision trees, k-NN, Random Forest)

Model Evaluation

Accuracy, recall, F1-score, MAE, RMSE, R²
Practical Workshop: train a simple model with Scikit-learn

Objective 2: Implement an End-to-End Project with Real Dataset

Mini-Project Launch (Group or Individual)

Dataset selection (health, education, environment, real estate...)
Data cleaning and preparation
Visualization and exploratory analysis

Guided Modeling Work

Train/test split, algorithm selection
Building and comparing multiple models (regression, classification, neural networks)
Results visualization and performance analysis

Participants' Presentations

Conclusion & Perspectives

Model limitations and best practices
Resources to advance in AI and ML

Free Lab 2

Machine Learning Embedded

Hands-on AI Implementation on Embedded Systems

Day One

Fundamentals & Platform Setup

Overview of AI in Embedded Electronics

Cloud AI vs. Embedded AI (Edge AI): Understanding the fundamental differences between cloud-based and edge computing AI solutions
Hardware Constraints: Memory limitations, energy efficiency considerations, and latency requirements
Use Cases: Intelligent IoT devices, autonomous objects, and predictive maintenance applications

Hands-on with Embedded Platform (Raspberry Pi)

GPIO Fundamentals: Digital inputs/outputs, pin configuration, and basic interfacing
Communication Protocols: PWM (Pulse Width Modulation), I2C, SPI, and UART interfaces
Sensor Integration: Reading data from IMU, temperature sensors, microphones, light sensors, and more

Neural Network Fundamentals

Introduction to neural network architecture, activation functions, forward propagation, and backpropagation concepts.

Building Your First ML Model

Data Collection: Gathering data through sensors and creating structured datasets
Dataset Creation: Data preprocessing, cleaning, and structuring for machine learning
Model Training: Building and training a simple Multi-Layer Perceptron (MLP) on PC or Google Colab
Model Evaluation: Understanding accuracy metrics, loss functions, and confusion matrices

Day Two

Practical Workshops & Implementation

CNN Design and Training

Comprehensive exploration of Convolutional Neural Networks (CNN) architecture, layer design, and optimization techniques for embedded systems.

CNN architecture fundamentals
Convolutional layers, pooling, and activation functions
Model optimization for resource-constrained devices

TensorFlow Implementation on Raspberry Pi

Learn to implement machine learning models using TensorFlow framework on Raspberry Pi hardware, including model conversion, optimization, and deployment strategies.

TensorFlow Lite for embedded systems
Model quantization and compression techniques
Real-time inference optimization

Object Recognition with Camera-equipped Robot

Practical Application: Build a complete vision system capable of detecting and classifying objects in real-time

Transfer Learning Implementation

Leverage pre-trained state-of-the-art models to accelerate development and improve performance. Learn to adapt existing models to your specific use cases.

VGG16

Deep convolutional network for image classification

AlexNet

Pioneer architecture in deep learning

ResNet

Residual networks with skip connections

And More

Explore various pre-trained architectures

Free Lab 3

Spring Boot REST API: Best Practices & Security with Keycloak

100% Practical Lab Session

Led by Ing/Dr. Mohamed Amine OMRANI

Overall Objective

Build a clean, maintainable, production-ready Spring Boot REST API, then secure it with Keycloak, applying best practices for API design, validation, error handling and security.

Day One (4 hours)

Spring Boot REST API Best Practices

REST Fundamentals Refresh

Resources and RESTful principles
HTTP verbs (GET, POST, PUT, DELETE, PATCH)
Status codes and their proper usage

API Design Conventions

URL naming conventions and best practices
Request/response structure standardization
Pagination, sorting, and filtering techniques
API versioning approaches

DTOs & Mapping

Avoid exposing entities directly
Model boundaries and separation of concerns
Consistent payload structures

Input Validation

Bean Validation annotations
Contract-first mindset
Clear validation messages

Error Handling

@ControllerAdvice for global exception handling
Consistent error format across the API
Proper HTTP status codes for different error scenarios

Day 1 Output

A simple CRUD API following conventions + validation + standardized errors

Day Two (4 hours)

Security with Keycloak in Spring Boot

Core Security Concepts

OIDC (OpenID Connect): Authentication layer on top of OAuth 2.0
JWT (JSON Web Tokens): Token structure and validation
Access vs Refresh Tokens: Token lifecycle management
Scopes vs Roles: Permission models and authorization

Keycloak Overview

Realm configuration and setup
Client registration and configuration
User management and authentication
Roles and permissions setup
Claim mappers (what to configure and why)

Spring Security Integration

Resource server configuration (JWT)
Stateless security setup
Configuration patterns and best practices

Authorization

Role-Based Access Control (RBAC)
Endpoint rules with @PreAuthorize
Public vs protected endpoints
Method-level security

Security Testing

Postman testing for protected endpoints
JWT in tests (approach and patterns)
Integration testing with security context

Day 2 Output

The same API secured with Keycloak, roles enforced, and security tests started

Learning Outcomes

By the end of this 8h training, participants will be able to:

Design consistent REST APIs (contracts, status codes, errors, pagination)

Apply Spring Boot best practices (DTOs, validation, architecture)

Secure APIs with Keycloak (OIDC, JWT, roles, access rules)

Deliver a more robust, production-ready API (quality + security mindset)

What You'll Gain

Benefits of attending these comprehensive training programs

Hands-on Experience

Practical experience with real hardware and software implementations

Certificate of Completion

Official certificate recognizing your participation and skills

Expert Instructors

Learn from experienced professionals in AI and embedded systems

Complete Toolkit

Access to all necessary hardware, software, and learning materials

Networking Opportunities

Connect with fellow researchers and industry professionals

Project Portfolio

Build working projects to showcase your new skills

Free Labs

AITEC'26 Free Training Sessions

AI for Scientific Research

Machine Learning Embedded

Spring Boot REST API: Best Practices & Security with Keycloak

AI for Scientific Research

Day One (4 hours)

Objective 1: Understanding AI Basics and Mastering Essential Tools

General Introduction

Installation and Tool Setup

Objective 2: Explore, Transform and Visualize Scientific Data

Advanced Pandas

Data Preprocessing

Advanced Visualization with Matplotlib

Day Two (4 hours)

Objective 1: Discover and Apply Main Supervised Algorithms

ML Project Steps (CRISP-DM in Detail)

Supervised Algorithms

Model Evaluation

Objective 2: Implement an End-to-End Project with Real Dataset

Mini-Project Launch (Group or Individual)

Guided Modeling Work

Participants' Presentations

Conclusion & Perspectives

Machine Learning Embedded

Day One

Overview of AI in Embedded Electronics

Hands-on with Embedded Platform (Raspberry Pi)

Neural Network Fundamentals

Building Your First ML Model

Day Two

CNN Design and Training

TensorFlow Implementation on Raspberry Pi

Object Recognition with Camera-equipped Robot

Transfer Learning Implementation

VGG16

AlexNet

ResNet

And More

Spring Boot REST API: Best Practices & Security with Keycloak

Overall Objective

Day One (4 hours)

REST Fundamentals Refresh

API Design Conventions

DTOs & Mapping

Input Validation

Error Handling

Day 1 Output

Day Two (4 hours)

Core Security Concepts

Keycloak Overview

Spring Security Integration

Authorization

Security Testing

Day 2 Output

Learning Outcomes

What You'll Gain

Hands-on Experience

Certificate of Completion

Expert Instructors

Complete Toolkit

Networking Opportunities

Project Portfolio

Useful Links

Newsletter

Address

Mail Us

Telephone

AITEC'26 Assistant