LEJ Knowledge Hub

Lecture

Right Arrow

SEARCH COURSES / LECTURES

Left Arrow

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

(121 Lectures Available)

S# Lecture Course Institute Instructor Discipline
51
  • Machine learning as optimization - gradient descent in one dimension (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
52
  • Model-based learning (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
53
  • Nearest neighbor models (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
54
  • Neural networks - activation functions (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
55
  • Neural networks - basic element (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
56
  • Neural networks - brief review of layers and backprop (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
57
  • Neural networks - layer definition (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
58
  • Neural networks - many layers (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
59
  • Neural networks - optimizing parameters - adagrad and adadelta (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
60
  • Neural networks - optimizing parameters - adam step-size update strategy (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
61
  • Neural networks - optimizing parameters - adaptive step-size (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
62
  • Neural networks - optimizing parameters - batch gradient descent training (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
63
  • Neural networks - optimizing parameters - momentum (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
64
  • Neural networks - optimizing parameters - running averages (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
65
  • Neural networks - output layer activation functions (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
66
  • Neural networks - regularization by batch normalization (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
67
  • Neural networks - regularization by early stopping and dropout (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
68
  • Neural networks - regularization by weight decay (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
69
  • Neural networks - training and back-propagation (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
70
  • Neural networks - weight initialization (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
71
  • Neural networks and Q-learning (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
72
  • Objectives of the reinforcement learning problem (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
73
  • One-dimensional linear regression - demo (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
74
  • Perceptron - overview of plan (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
75
  • Perceptron convergence theorem (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences

Basic and Health Sciences

Applied Sciences

Social Sciences