Lesson 33 - Module 12 Machine Learning Version 1 CSE IIT,...

Info iconThis preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon
Module 12 Machine Learning Version 1 CSE IIT, Kharagpur
Background image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should learn about taxonomy of learning systems Students should learn about different aspects of a learning systems like inductive bias and generalization The student should be familiar with the following learning algorithms, and should be able to code the algorithms o Concept learning o Decision trees o Neural networks Students understand the merits and demerits of these algorithms and the problem domain where they should be applied. At the end of this lesson the student should be able to do the following: Represent a problem as a learning problem Apply a suitable learning algorithm to solve the problem. Version 1 CSE IIT, Kharagpur
Background image of page 2
Lesson 33 Learning : Introduction Version 1 CSE IIT, Kharagpur
Background image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
12.1 Introduction to Learning Machine Learning is the study of how to build computer systems that adapt and improve with experience. It is a subfield of Artificial Intelligence and intersects with cognitive science, information theory, and probability theory, among others. Classical AI deals mainly with deductive reasoning, learning represents inductive reasoning. Deductive reasoning arrives at answers to queries relating to a particular situation starting from a set of general axioms, whereas inductive reasoning arrives at general axioms from a set of particular instances. Classical AI often suffers from the knowledge acquisition problem in real life applications where obtaining and updating the knowledge base is costly and prone to errors. Machine learning serves to solve the knowledge acquisition bottleneck by obtaining the result from data by induction. Machine learning is particularly attractive in several real life problem because of the following reasons: Some tasks cannot be defined well except by example Working environment of machines may not be known at design time Explicit knowledge encoding may be difficult and not available Environments change over time Biological systems learn Recently, learning is widely used in a number of application areas including, Data mining and knowledge discovery Speech/image/video (pattern) recognition
Background image of page 4
Image of page 5
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 09/20/2010 for the course MCA DEPART 501 taught by Professor Hemant during the Fall '10 term at Institute of Computer Technology College.

Page1 / 9

Lesson 33 - Module 12 Machine Learning Version 1 CSE IIT,...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online