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Introduction to Machine Learning (Fall 2020) (M-I-T)

(121 Lectures Available)

S# Lecture Course Institute Instructor Discipline
76
  • Perceptron through origin algorithm (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
77
  • Policy search (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
78
  • Proof sketch of the perceptron convergence theorem (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
79
  • Q-learning (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
80
  • Q-learning select-action strategies (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
81
  • Random forests models (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
82
  • Recommender systems - introduction (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
83
  • Recurrent neural network model (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
84
  • Regression - analytical minimization of the ridge regression objective (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
85
  • Regression - beauty of the closed form OLS solution (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
86
  • Regression - OLS analytical solution setup (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
87
  • Regression - OLS analytical solution using gradients (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
88
  • Regression - OLS and gradient descent demo example (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
89
  • Regression - ordinary least squares solution using optimization (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
90
  • Regression - regularization by ridge regression (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
91
  • Regression - ridge regression using gradient descent (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
92
  • Regression - stochastic gradient descent (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
93
  • Regression - structural error and estimation error (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
94
  • Regression and the ordinary least squares problem (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
95
  • Regression trees - problem statement (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
96
  • Reinforcement learning demos (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
97
  • RNNs - gating mechanisms and LSTM (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
98
  • RNNs - training a language model (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
99
  • Sequence-to-sequence RNN (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
100
  • Sequential models - state machines (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences

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