Course Description

 

Keynote

  • Courses (to be completed)



  • Maja Pantic
    (Imperial College London) []
    Artificial Emotional Intelligence, Faces, Deep Fakes and Other Topics


    Rick S. Blum
    (Lehigh University) [introductory/intermediate]
    Deep Learning and Cybersecurity


    Ben Brown
    (Lawrence Berkeley National Laboratory) [introductory/advanced]
    Explainable AI (XAI) Techniques for Science and Engineering - Toward Statistical Inference for the 21st Century


    Georgios Giannakis
    (University of Minnesota) [advanced]
    Ensembles for Interactive and Deep Learning Machines with Scalability, Expressivity, and Adaptivity


    Çağlar Gülçehre
    (DeepMind) [intermediate/advanced]
    Deep Reinforcement Learning


    Vincent Lepetit
    (ENPC ParisTech) [intermediate]
    Deep Learning and 3D Geometry


    Geert Leus
    (Delft University of Technology) [introductory/intermediate]
    Graph Signal Processing: Introduction and Connections to Distributed Optimization and Deep Learning


    Abdelrahman Mohamed
    (Facebook AI Research) [introductory/advanced]
    Recent Advances in Automatic Speech Recognition


    Massimiliano Pontil
    (Italian Institute of Technology) [intermediate/advanced]
    Statistical Learning Theory


    Jose Principe
    (University of Florida) [intermediate/advanced]
    Cognitive Architectures for Object Recognition in Video


    Fedor Ratnikov
    (National Research University Higher School of Economics) [introductory]
    Specifics of Applying Machine Learning to Problems in Natural Science


    Salim Roukos
    (IBM Research AI) [intermediate/advanced]
    Deep Learning Methods for Natural Language Processing


    Björn Schuller
    (Imperial College London) [introductory/intermediate]
    Deep Signal Processing


    Alex Smola
    (Amazon) [introductory/advanced]
    Dive into Deep Learning


    Sargur N. Srihari
    (University at Buffalo) [introductory]
    Generative Models in Deep Learning


    Kunal Talwar
    (Google Brain) [intermediate]
    Differentially Private Machine Learning