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Advanced AI for Data Analysis

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Advanced AI for Data Analysis

The volume of data generated on the Internet is growing exponentially. Incredible amounts of unstructured data such as texts, images, time series, are available, all being produced at an overwhelming pace. In recent years, the availability of very high processing power (i.e., GPU’s), combined with industrial interest, has given rise to new impressive AI methods, such as deep and reinforcement learning, which allow to extract significant information from large scale data in a very short time, solving highly complex problems and opening new perspectives. 

The Advanced AI Program is designed to provide deep insights on these new methods and their applications to massive and highly heterogeneous data. Important applications of these methods include marketing, gaming, recommendation systems, text mining, neuro-linguisting programming, social networks, fraud detection, image and video recognition, etc.

  • Dates :From January 16th to February 29th, 2020
  • Durée :11 days
  • Pour qui :Technical engineers, project managers, data scientists
  • Lieu :Ecole Polytechnique Executive Education
  • Langue :English
  • Certification :Lead a data science project using advanced AI tools

Objectifs

  1. This Program offers a comprehensive in-depth experience, with hands-on and presentation of the state-of-the-art AI techniques for large-scale data. The course covers deep learning for text, graph and time series mining; NLP; influence maximization; and recommendation algorithms.

Programme

INTRODUCTION TO DATA MODALITIES AND LEGAL ASPECTS

• Consumer analysis • Commercial offers • Client behaviour • Influencers • Legal aspects

 

DATA SCIENCE TOOLS - BIG DATA CONTEXT

• Data base • Spark • Hadoop • Data science project pipeline • Exploration • Feature selection • Preprocessing Dimensionality reduction

 

DEEP LEARNING

• Introduction to advanced deep learning • Optimization of DL architectures • Attention based architectures • Transformers • Autoencoders for unsupervised learning

 

AI FOR TEXT MINING AND NLP

• Architecture of web search engines • Advanced Machine learning for text and NLP • Deep learning methods for NLP • Word and Word/document and contextual word embeddings (ELMO, BIRT) • Automated summarization • Entity recognition • Chatbots

 

AI FOR GRAPHS AND TIME SERIES

• Node/graph embeddings • Graph kernels • Graph neural networks • Graph autoencoders • Deep sets

 

INFLUENCE MAXIMIZATION FOR SOCIAL/COLLABORATION GRAPHS

• SIR/SIS • Greedy algorithms • IMM (influence maximization via martingales) • Stop and stare kai SKIM • Graph degeneracy based methods (D-core, RCG)

 

RECOMMENDATION ALGORITHMS

• Factorization machines • NMF • Neural Collaborative Filtering • Deep Factorization Machines Autoencoder based • Randomized SVD and SVD++

 

DATA CHALLENGE

• Potential topics: Opinion mining • Product recommendation • Link prediction • Chatbots…

 

CERTIFICATION

  1. Titre: 
    Requirements
    Texte: 

    Skills and knowledge in data science (mathematics and machine learning) and programming (Python)

  2. Titre: 
    Competencies
    Texte: 

    › Acquire skills on recent machine learning methods
    › Master machine/deep learning tools and methodologies to address problems in text mining and NLP
    › Master machine/deep learning methods for graphs: link prediction, graph/node classification
    › Master AI algorithms to develop online marketing, fraud detection and knowledge extraction algorithms from web data
    › Integrate technical possibilities and ecosystem issues to characterize a data science-based project (massive data)
    › Analyze the opportunities, challenges and impacts associated

Intervenants

  1. Responsable pédagogique
    Michalis
    Professeur au Laboratoire d'Informatique de l'École polytechnique (LIX)

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