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

(121 Lectures Available)

S# Lecture Course Institute Instructor Discipline
26
  • Demo example - RNNs (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
27
  • Evaluating hypotheses - training set error (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
28
  • Evaluating learning algorithms - validation (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
29
  • Evaluating predictions - loss functions (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
30
  • Example of perceptron algorithm with polynomial basis transformations (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
31
  • Feature representation - polynomial basis (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
32
  • Feature representation - transforming through-origin to not-through-origin (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
33
  • Feature representation strategies for dealing with varied data (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
34
  • Gradient descent optimization - algorithm in multiple dimensions (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
35
  • Gradient descent optimization - algorithm in one dimension (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
36
  • Gradient descent optimization - local optima (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
37
  • Gradient descent optimization - parameters and demo (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
38
  • Introduction to ML - estimation and generalization (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
39
  • Introduction to ML - perspective and history (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
40
  • Introduction to non-parametric models (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
41
  • Introduction to reinforcement learning (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
42
  • K-armed bandits (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
43
  • Learning algorithms (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
44
  • Linear classifiers (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
45
  • Linear logistic classifier - a few comments about regularization (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
46
  • Linear logistic classifier - hypothesis class (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
47
  • Linear logistic classifier - negative log likelihood loss function (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
48
  • Logistic regression - setting and sigmoid function (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
49
  • Machine learning as optimization - framework (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
50
  • Machine learning as optimization - gradient descent in multiple dimensions (M-I-T)
Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences

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