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S# Lecture Course Institute Instructor Discipline
1751
SM – MDP – computing the value function (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1752
SM – MDP – finding an optimal policy (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1753
SM – MDP – finite horizon and the value function (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1754
SM – MDP – Grid world example demos (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1755
SM – MDP – infinite-horizons and the value iteration algorithm (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1756
SM – MDP – the reward and policy functions (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1757
SM – MDP – the transition function (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1758
SM – State machine as a transducer (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1759
AI Bias and Fairness (M-I-T)
Deep Learning (2020) (M-I-T) MIT Ava Soleimany Applied Sciences
1760
SM – Towards recurrent neural networks (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1761
Barack Obama: Intro to Deep Learning  (M-I-T)
Deep Learning (2020) (M-I-T) MIT Barack Obama Applied Sciences
1762
Supervised learning – hypotheses (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1763
Beyond Deep Learning: Learning+Reasoning (M-I-T)
Deep Learning (2020) (M-I-T) MIT Lisa Amini Applied Sciences
1764
Biologically Inspired Neural Networks (IBM) (M-I-T)
Deep Learning (2020) (M-I-T) MIT Dmitry Krotov Applied Sciences
1765
Supervised learning – setting (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1766
The perceptron algorithm (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1767
Computer Vision Meets Social Networks (M-I-T)
Deep Learning (2020) (M-I-T) MIT Lin Ma Applied Sciences
1768
The perceptron algorithm in action – an example (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1769
Convolutional Neural Networks (M-I-T)
Deep Learning (2020) (M-I-T) MIT Ava Soleimany Applied Sciences
1770
Deep CPCFG for Information Extraction (M-I-T)
Deep Learning (2020) (M-I-T) MIT Nigel Duffy Applied Sciences
1771
The random linear classifier algorithm (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1772
Deep Generative Modeling (M-I-T)
Deep Learning (2020) (M-I-T) MIT Ava Soleimany Applied Sciences
1773
Theory of perceptron – Linear separability (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1774
Theory of perceptron – margin of a dataset (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
1775
Deep Learning – A Personal Perspective (M-I-T)
Deep Learning (2020) (M-I-T) MIT Urs Muller Applied Sciences