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

(121 Lectures Available)

S# Lecture Course Institute Instructor Discipline
1
  • Back-propagation through time - backwards pass (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
2
  • Back-propagation through time - forward pass (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
3
  • Back-propagation through time - weight updates (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
4
  • Building a tree - greedy algorithm (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
5
  • Building a tree - minimum error splits (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
6
  • Building a tree - pruning (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
7
  • Classification trees (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
8
  • Classification trees - impurity measures - entropy (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
9
  • Classification trees - impurity measures - gini index (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
10
  • CNNs - a specific illustrative example filter (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
11
  • CNNs - backprop and gradient descent (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
12
  • CNNs - convolutional neural network layers (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
13
  • CNNs - convolutional neural networks - intro (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
14
  • CNNs - max pooling (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
15
  • CNNs - one-dimensional filters (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
16
  • CNNs - two-dimensional filters (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
17
  • CNNs - typical architecture (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
18
  • Collaborative filtering - alternating least squares algorithm (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
19
  • Collaborative filtering - alternating least squares idea (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
20
  • Collaborative filtering - framework (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
21
  • Collaborative filtering - hypothesis space (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
22
  • Collaborative filtering - objective function (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
23
  • Collaborative filtering - stochastic gradient descent (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
24
  • Collaborative filtering - strategy (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
25
  • Decision trees (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences

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