Program Overview

Advanced AI for Data Analysis Program

Introduction

The Advanced AI for Data Analysis program offered by École Polytechnique Executive Education is designed to provide participants with a comprehensive understanding of advanced methods in artificial intelligence applied to massive and heterogeneous data. The program aims to equip professionals with the skills to analyze, exploit, and valorize large-scale data.

Program Content

The program covers a range of topics, including:

• Introduction to the AI and Data Science Ecosystem: This module provides an overview of the current state of data science and AI, including opportunities, challenges, and considerations for projects involving massive and unstructured data.
• Deep Learning for Text and Natural Language Processing (NLP): This module focuses on methods and tools for preprocessing, indexing, searching, and classifying texts within documents or collections.
• AI Applied to Graphs and Social Networks: This module explains and implements advanced methods and tools for preprocessing graphs, searching, and classifying them, as well as evaluating nodes or communities.
• AI for Time Series, Images/Vision, and Recommendations for Web Marketing: This module provides tools and methodologies for predicting time series data, presents the state of the art in recommendation methods, and introduces AI methods for images and computer vision applications.
• Generative AI and Large Pre-Trained Models: This module exposes participants to the latest developments in generative AI, including recent models and their applications in language and multimodal generation.
• Data Challenge: This module applies the techniques learned in previous courses to a case study from an industrial or academic problem, allowing participants to implement the methodology and techniques seen in the modules.

Pedagogical Objectives

Upon completion of the program, participants will be able to:

• Apply recent machine learning and deep learning techniques to complex data
• Exploit advanced algorithms for graph analysis
• Use natural language processing for text mining and semantic analysis
• Develop AI solutions for e-commerce and knowledge extraction from the web
• Integrate technical and ecosystem dimensions in the design of data science projects
• Identify opportunities, challenges, and impacts related to AI in various industrial sectors

Target Audience

This program is designed for professionals with initial experience in data science, particularly in machine learning, deep learning, unstructured data, NLP, and graph mining. It is ideal for:

• Project managers
• Consultants
• Data scientists
• Data analysts

Prerequisites

Participants should have a good understanding of data science (algorithms, supervised and unsupervised learning), programming skills (Python), computer science (databases), and mathematics (statistics, probability, linear algebra).

Practical Modalities

• Duration: 11 days (84 hours)
• Format: 100% distance learning
• Language: Teaching and support in English

Instructors

• Michalis Vazirgiannis: Professor and researcher at the Computer Science Department of École Polytechnique, specializing in data mining and machine/deep learning with industrial applications to large-scale data.
• Giannis Nikolentzos: Post-doctoral researcher at the Computer Science Laboratory of École Polytechnique, with a Ph.D. in Graph Mining from Athens University of Economics and Business.

Outcome Statistics

• Certification Presentation Rate: 92% over the last two promotions
• Certification Success Rate: 80%*
• Satisfaction Rating: 4.5/5*
• Average Number of Participants per Session: 8 participants*

*Based on the six last promotions.