PASCAL Bootcamp in Machine Learning, Marseille 2010

Introduction to Machine Learning

author: Iain Murray, School of Informatics, University of Edinburgh
published: Aug. 5, 2010, recorded: July 2010, views: 10108

Slides

Slides
0:00	Introduction to Machine Learning
1:40	Face detection
3:47	What do we do? (1)
3:56	What do we do? (2)
4:44	Response surface optimization
6:49	Speech recognition
7:35	Recommender systems
9:02	Roadmap
10:00	Oranges and Lemons
11:01	A two-dimensional space
12:19	Stars and Galaxies
12:48	Linear classier
13:28	The weight vector (1)
17:10	The weight vector (2)
18:44	Learning the weights (1)
20:34	Learning the weights (2)
22:25	Implementing the bias
23:55	Output of the perceptron
24:17	The perceptron
25:27	K-Nearest neighbours classier
27:40	The odd-ball orange
28:25	Decision boundaries
29:06	LANDSAT application
31:43	Parametric vs. non-parametric
33:44	Linear classier revisited
34:31	Nonlinear basis functions
36:47	Batch supervised learning
39:05	Linear regression
40:55	Linear regression (with features)
43:19	Neighbour-based regression
44:36	Simplest kernel smoothing (1)
45:06	Simplest kernel smoothing (2)
45:54	Least squares classier (1)
46:59	Least squares classier (2)
47:49	Roadmap

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.

Lecture popularity: You need to login to cast your vote.

Watch videos: (click on thumbnail to launch)

Part 1 49:14

!NOW PLAYING

Part 2 51:57

Part 3 1:27:21

Description

How can we represent data on a computer and use it to learn to perform useful tasks? This lecture reviews some simple classification and regression rules, discusses under- and over-fitting and emphasises the utility of defining objective functions for learning. There is also a short overview of Bayesian learning, and some practical tips for pre-processing and visualizing data. The lecture ends with a brief mention of unsupervised learning and related topics.