Machine Learning is Everywhere?

What Is Machine Learning?

Basic Paradigm

Similarity Based on Weight

Similarity Based on Height

Clustering using Unlabeled Data

Feature Representation

An Example

Measuring Distance Between Animals

Minkowski Metric

Euclidean Distance Between Animals

Add an Alligator

Using Binary Features

Fitting Three Clusters Unsupervised

Classification approaches

Confusion Matrices (Training Error)

Training Accuracy of Models

Applying Model to Test Data

MIT OpenCourseWare

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016
View the complete course: http://ocw.mit.edu/6-0002F16
Instructor: Eric Grimson

In this lecture, Prof. Grimson introduces machine learning and shows examples of supervised learning using feature vectors.

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

11. Introduction to Machine Learning

11. Introduction to Machine Learning

11. Introduction to Machine Learning

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