Marlos C. Machado, Ph.D.
Pronouns: He, Him, His
Contact
Assistant Professor, Faculty of Science - Computing Science
- machado@ualberta.ca
Overview
Area of Study / Keywords
Artificial Intelligence Machine Learning Reinforcement Learning
About
Education
- B.Sc., Computer Science, Universidade Federal de Minas Gerais (UFMG), 2010
- M.Sc., Computer Science, Universidade Federal de Minas Gerais (UFMG), 2013
- Ph.D., Computing Science, University of Alberta, 2019
Research
Summary
Marlos C. Machado is an assistant professor at the University of Alberta. He is also an Amii fellow, where he also holds a Canada CIFAR AI Chair. Marlos’s research mostly focuses on the problem of reinforcement learning. He received his B.Sc. and M.Sc. from UFMG, in Brazil, and his Ph.D. from the University of Alberta, where he popularized the idea of temporally-extended exploration through options. He was a researcher at DeepMind from 2021 to 2023 and at Google Brain from 2019 to 2021, during which time he made major contributions to reinforcement learning, in particular the application of deep reinforcement learning to control Loon’s stratospheric balloons. Marlos’s work has been published in the leading conferences and journals in AI, including Nature, JMLR, JAIR, NeurIPS, ICML, ICLR, and AAAI. His research has also been featured in popular media such as BBC, Bloomberg TV, The Verge, and Wired.
Courses
CMPUT 365 - Introduction to Reinforcement Learning
This course provides an introduction to reinforcement learning, which focuses on the study and design of learning agents that interact with a complex, uncertain world to achieve a goal. The course will cover multi- armed bandits, Markov decision processes, reinforcement learning, planning, and function approximation (online supervised learning). The course will take an information-processing approach to the study of intelligence and briefly touch on perspectives from psychology, neuroscience, and philosophy. The course will use the University of Alberta MOOC on Reinforcement Learning. Any student who understands the material in this course will understand the foundations of much of modern probabilistic artificial intelligence (AI) and be prepared to take more advanced courses, or to apply AI tools and ideas to real-world problems. Prerequisites: CMPUT 175 or 275; one of CMPUT 267, 466, or STAT 265.
CMPUT 628 - Topics in Machine Learning