DeepLearningMaterials
Some slides/materials from a DeepLearning summerschool (2019)
Note: I take no credits either for the course description either for the material itself, this is just a repository for my personal use (storage).
Keynotes
Maria-Florina Balcan (Carnegie Mellon University): Data Driven Clustering
Mark Gales (University of Cambridge): Use of Deep Learning in Non-native Spoken English Assessment
Mihaela van der Schaar (University of Cambridge): Learning Engines for Healthcare: Using Machine Learning to Transform Clinical Practice and Discovery
Courses
Aaron Courville (University of Montréal) [introductory/intermediate]: Deep Generative Models
Issam El Naqa (University of Michigan) [introductory/intermediate]: Deep Learning for Biomedicine
Qiang Ji (Rensselaer Polytechnic Institute) [introductory/intermediate]: Probabilistic Deep Learning for Computer Vision
James Kwok (Hong Kong University of Science and Technology) [introductory/intermediate]: Compressing Neural Networks
Tomas Mikolov (Facebook) [introductory]: Using Neural Networks for Modeling and Representing Natural Languages (with Piotr Bojanowski and Armand Joulin)
Hermann Ney (RWTH Aachen University) [intermediate/advanced]: Speech Recognition and Machine Translation: From Statistical Decision Theory to Machine Learning and Deep Neural Networks
Jose C. Principe (University of Florida) [intermediate/advanced]: Cognitive Architectures for Object Recognition in Video
Fabio Roli (University of Cagliari) [introductory/intermediate]: Adversarial Machine Learning
Björn Schuller (Imperial College London) [introductory/intermediate]: Deep Learning for Intelligent Signal Processing
Alex Smola (Amazon) [introductory]: Dive into Deep Learning
Sargur Srihari (University at Buffalo) [intermediate/advanced]: Explainable Artificial Intelligence
Ponnuthurai N Suganthan (Nanyang Technological University) [introductory/intermediate]: Learning Algorithms for Classification, Forecasting and Visual Tracking
Johan Suykens (KU Leuven) [introductory/intermediate]: Deep Learning, Neural Networks and Kernel Machines
Bertrand Thirion (INRIA) [introductory]: Understanding the Brain with Machine Learning
Gaël Varoquaux (INRIA) [intermediate]: Representation Learning in Limited Data Settings
René Vidal (Johns Hopkins University) [intermediate/advanced]: Mathematics of Deep Learning
Haixun Wang (WeWork) [intermediate]: Abstractions, Concepts, and Machine Learning
Xiaowei Xu (University of Arkansas, Little Rock) [introductory/advanced]: Multi-resolution Models for Learning Multilevel Abstract Representations of Text
Ming-Hsuan Yang (University of California, Merced) [intermediate/advanced]: Learning to Track Objects
Zhongfei Zhang (Binghamton University) [introductory/advanced]: Knowledge Discovery from Complex Data with Deep Learning