
Yi Ma
On the Principles of Parsimony and Self-Consistency: Structured Compressive Closed-Loop Transcription

Daphna Weinshall
Curriculum Learning in Deep Networks

Eric P. Xing
It Is Time for Deep Learning to Understand Its Expense Bills [virtual]
Lecturers

Matias Carrasco Kind
[intermediate] Anomaly Detection

Nitesh Chawla
[introductory/intermediate] Graph Representation Learning

Sumit Chopra
[intermediate] Deep Learning for Healthcare

Luc De Raedt
[introductory/intermediate] From Statistical Relational to Neuro-Symbolic Artificial Intelligence

Marco Duarte
[introductory/intermediate] Explainable Machine Learning

João Gama
[introductory] Learning from Data Streams: Challenges, Issues, and Opportunities

Claus Horn
[intermediate] Deep Learning for Biotechnology

Zhiting Hu & Eric P. Xing
A “Standard Model” for Machine Learning with All Experiences [virtual]

Nathalie Japkowicz
[intermediate/advanced] Learning from Class Imbalances

Gregor Kasieczka
[introductory/intermediate] Deep Learning Fundamental Physics: Rare Signals, Unsupervised Anomaly Detection, and Generative Models

Karen Livescu
[intermediate/advanced] Speech Processing: Automatic Speech Recognition and beyond

David McAllester
[intermediate/advanced] Information Theory for Deep Learning

Dhabaleswar K. Panda
[intermediate] Exploiting High-performance Computing for Deep Learning: Why and How?

Fabio Roli
[introductory/intermediate] Adversarial Machine Learning

Bracha Shapira
[introductory/intermediate] Recommender Systems

Kunal Talwar
[introductory/intermediate] Foundations of Differentially Private Learning

Tinne Tuytelaars
[introductory/intermediate] Continual Learning in Deep Neural Networks

Lyle Ungar
[intermediate] Natural Language Processing using Deep Learning

Bram van Ginneken
[introductory/intermediate] Deep Learning for Medical Image Analysis

Yu-Dong Zhang
[introductory/intermediate] Convolutional Neural Networks and Their Applications to COVID-19 Diagnosis