This document provides an overview of machine learning including its definition, components that can be learned, and common machine learning methods. It discusses how machine learning can be applied to problems like autonomous driving, speech recognition, and personalized news recommendations. The document outlines general steps in a machine learning process and provides an example of using the K-means clustering algorithm to classify movie critics. It also describes how machine learning could be used for problems like recycling sorting, spam filtering, and product recommendations.

1. Machine Learning

2. Machine Learning
Definition:
The ability of a machine to improve its performance
based on previous results.

3. Components to be learned
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Direct-mapping from conditions on the current state to actions
Means to infer properties from the percept sequence
Information about the way the world evolves
Utility information on desirability of world states
Action information
Goals describe the maximum achievement

4. Machine Learning methods
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Symbol-based – symbols represent entities & relationships
Connectionist – patterns of activity in networks
Genetic – Imitation of genetic & evolutionary process
Stochastic – Insight that support Bayes' rule

5. Exercise

6. How to locate the license plate area
and recognize the license plate info?

7. Why machine learning?
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Recent progress in algorithms & theory
Growing flood of online data
Computational power is available
Budding industry

8. Three niches on machine learning
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Data mining: using historical data to improve decision
Software applications we can't program by hand
– Autonomous driving
– Speech recognition
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Self customizing programs
- Newsreader that learns user interests

9. General steps in ML
Problem
statement
Performance
evaluation
Feature
extraction
ML
implementation

10. Example problem: movie critics
Example method: K-means clustering
algorithm

11. Group discussion
Group 1 – amira, meng kwang, nogol, musobi, hadi
Problem: ML for recycling centre – paper, glass, plastic

12. Group discussion
Group 2 – airil, bryan,afsaneh2,farzaneh, azleen
Problem: ML for junk email filter

13. Group discussion
Group 3 – malina, syaza, laith, afsaneh1,
mehrdad
Problem: ML for electronics gadget
recommendation system ( can be specific –
smartphone or tablet)

14. Group discussion – expected
discussion
1. Explanation on problem statement
2. List of appropriate features
3. Chosen features (significant)
4. ML Method
5. Expected results

15. Class discussion
1. How do algorithms make recommendations from data?
2. Why are features important?
3. Would K-means work the same with more than 2 features?
4. Could we visualize more than 2 features? More than 3?
5. Think of how Euclidean Distance is calculated. Do all the
features need to be on the same scale?
6. What are challenges in solving your problem?