Tommi Jaakkola

Tommi S. Jaakkola received M.Sc. in theoretical physics from Helsinki University of Technology and Ph.D. from MIT in computational neuroscience. He joined MIT faculty 1998 and he is now the Thomas Siebel Professor in EECS and IDSS at MIT.
His research covers theory, algorithms, and applications of machine learning, from statistical inference and estimation to natural language processing, computational biology, as well as recently machine learning for chemistry. His awards include Sloan research fellowship, AAAI Fellow, and many publication awards across the research areas.

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Machine Learning with Python: from Linear Models to Deep Learning (edX)

Sep 6th 2021
Machine Learning with Python: from Linear Models to Deep Learning (edX)
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An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. Machine learning methods are commonly used across engineering and sciences, from computer systems to physics. Moreover, commercial sites such as search engines, recommender systems (e.g., Netflix, Amazon), [...]
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