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

(121 Lectures Available)

S# Lecture Course Institute Instructor Discipline
1 Introduction to ML - perspective and history (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
2 Linear classifiers (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
3 The random linear classifier algorithm (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
4 Machine learning as optimization - framework (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
5 Logistic regression - setting and sigmoid function (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
6 Linear logistic classifier - hypothesis class (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
7 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
8 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
9 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
10 One-dimensional linear regression - demo (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
11 Two-dimensional linear regression - demo (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
12 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
13 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
14 Gradient descent optimization - local optima (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
15 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
16 Gradient descent optimization - parameters and demo (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
17 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
18 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
19 Regression - OLS analytical solution setup (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
20 Regression - OLS analytical solution using gradients (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
21 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
22 Regression - regularization by ridge regression (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
23 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
24 Regression - ridge regression using gradient descent (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
25 Regression - stochastic gradient descent (M-I-T) Introduction to Machine Learning (Fall 2020) (M-I-T) MIT Prof. Leslie Kaelbling Applied Sciences
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of 5 121 Lectures Available.