Introduction to Machine Learning

author: John Quinn, Faculty of Computing and Informatics Technology, Makerere University
published: March 31, 2011,   recorded: February 2011,   views: 4755
Categories

Slides

Related content

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.
  Bibliography

 Watch videos:   (click on thumbnail to launch)

Watch Part 1
Part 1 56:59
!NOW PLAYING
Watch Part 2
Part 2 57:31
!NOW PLAYING
Watch Part 3
Part 3 50:55
!NOW PLAYING

Description

This talk gives an overview of machine learning from a practical perspective. Starting with examples of problems we might want to solve (in vision, signal processing, and geospatial inference), and the assumptions we have to make in order to get anywhere, it then covers a number of different supervised and unsupervised learning techniques. The talk concludes with ideas on how to evaluate a system, and when we should believe that a model is "right".

See Also:

Download slides icon Download slides: aibootcamp2011_quinn_iml.pdf (3.9┬áMB)


Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 Ozgur, June 17, 2011 at 1:42 p.m.:

The best web site addresses for computer science.
Texts, videos, graphics, questions about computer sciences.
Online computer science textbooks. Video audio lectures.
http://www.scientificpages.net/comput...


Comment2 karanja Evanson, February 10, 2014 at 6:20 p.m.:

Thats truly a cookbook approach John

Machine learning is great

Write your own review or comment:

make sure you have javascript enabled or clear this field: