This document provides an introduction to machine learning. It is presented by Dr. Muhammad Umar Chaudhry and discusses key concepts including what learning is, examples of machine learning applications, and different types of machine learning problems. The document also outlines the key ingredients needed for machine learning, including data, experience, and a learning model. It describes different types of learning methods such as supervised, unsupervised, semi-supervised, and reinforcement learning.

1. Machine Learning
Instructor: Dr. Muhammad Umar Chaudhry

2. Machine Learning 2
What is Learning?
“Learning is a process that leads to change, which occurs as a
result of experience and increases the potential for improved
performance and future learning.” 1
1. Ambrose et al, 2010. How Learning Works: Seven Research-Based Principles for Smart Teaching

3. Machine Learning 3
What is Learning?
“Learning is any relatively permanent change in behavior that
occurs as a result of experience.”
(S. P. Robbins)
“Learning is the modification of behavior through experience
and training.”
(Biswanath Ghosh)

4. Machine Learning 4
Learning Examples
• Eat
• Walk
• Read
• Drive
• Recognize
Can you think of any other 5 learning examples?

5. Machine Learning 5
Learning Experience
• How humans learn?
Wheels # Wheels Doors Window
Yes 4 Yes Yes
CAR CAR
CAR

6. Machine Learning 6
What is Machine Learning?
Machine Learning is the subfield of computer science that
gives “computers the ability to learn without being explicitly
programmed .”
(Arthur Samuel, 1959)
• To improve the performance of programs based on
given data, previous results, or experiences
– Developing methods to extract knowledge from examples
– Methods for creating computer programs by the analysis of data
sets

7. Machine Learning 7
How Machines Can Learn?

8. Machine Learning 8
Key Ingredients
• Data
• Experience
• Learning Model
Data is cheap and abundant (data warehouses, data
marts); knowledge is expensive and scarce

9. Machine Learning 9
Machine Learning Applications
• Voice/Face/Fingerprint/Iris/DNA/Signature recognition
• Web-search, Document & information retrieval, Machine
translation
• Recommendation, Spam filter
• Credit card fraud detection, Loan application analysis
• Marketing, Stock market prediction
• Games: Chess
• …

10. Machine Learning 10
Types of ML Problems (1)
• Classification
 Voice/Face/Fingerprint/Iris/DNA/Signature recognition
 Recommendation,
 Spam filter
 Credit card fraud detection
• Regression
 Stock market prediction
 House price prediction
• Clustering
 Web-search, Document & information retrieval
 User segmentation
• Strategy Learning
 Games
• Association
 POS Analysis

11. Machine Learning 11
Types of ML Problems (2)
• Classification - 1
 Each given data has its own class or label
 Once a query is given, a system should tell the class of the query
 For example: Security Gate
ORL dataset, AT&T Laboratories, Cambridge UK
Permitted
Persons
Query: Permitted or Not?

12. Machine Learning 12
Types of ML Problems (6)
• Regression - 2
 The process of predicting continuous values
House ID Area (sq. ft) # Bed # Bath Price
1 2700 2 2 5000000
2 2000 3 4 5500000
3 2200 3 3 6000000
4 1500 2 2 3500000
5 1800 3 2 4000000
6 1200 2 1 3000000
7 2500 4 4 6500000
House Price dataset

13. Machine Learning 13
Types of ML Problems (7)
• Clustering - 1
 A set of un-labeled data is given
 Your program should group the data (Finding hidden structure of
data)
 If a query is given, your program should determine the group in
which the query belongs to
?

14. Machine Learning 14
Types of Learning Methods
• Supervised Learning
• Classification, Regression
• All given data is labeled
• We “teach the model”, then with that knowledge, it can predict the unknown
or future instances
• Unsupervised Learning
• Clustering, Dimension Reduction, Association
• Data is not labeled
• The model works on its own to discover information
• Semi-supervised Learning
• Classification, Clustering
• Some data is labeled, and some is not
• Reinforcement Learning
• Strategy Learning
• Reward is given to your behaviors

15. Machine Learning 15
Important Concepts (1)
Attributes/Features
Samples/Instances/Examples
Target Variable

16. Machine Learning 16
Important Concepts (2)