Probabilistic Graphical Models[Course Website]
Many of the problems in artificial intelligence, statistics, computer systems, computer vision, natural language processing, and computational biology, among many other fields, can be viewed as the search for a coherent global conclusion from local information. The probabilistic graphical models framework provides an unified view for this wide range of problems, enables efficient inference, decision-making and learning in problems with a very large number of attributes and huge datasets. This graduate-level course will provide you with a strong foundation for both applying graphical models to complex problems and for addressing core research topics in graphical models.
Introduction to Machine Learning[Course Website]
Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Linear Regression, SVMs, Neural Networks, Graphical Models, Clustering, etc. Programming assignments include hands-on experiments with various learning algorithms. This course is designed to give a PhD-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.
CIAIOn the Utility of Gradient Compression in Distributed Training SystemsCIAI Colloquium, MBZUAI 2022
BaiduFrom Learning, to Meta-Learning, to "Lego-Learning” – theory, system, and applicationsBaidu 2021
KDD DLDIt is time for deep learning to understand its expense bills
ACL Meta-NLPLearning-to-learn through Model-based Optimization: HPO, NAS, and Distributed Systems
ICML ML4DataA Data-Centric View for Composable Natural Language Processing