Understanding Machine Learning: Principles, Applications, and Types, Slides of Computer science

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Machine Learning

Instructor: Dr. Muhammad Umar

Chaudhry

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

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)

Learning Examples

• Eat

• Walk

• Read

• Drive

• Recognize

Can you think of any other 5 learning examples?

Learning Experience

• How humans learn?

Wheels # Wheels Doors Window Yes 4 Yes Yes

CAR CAR CAR

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

How Machines Can Learn?

Key Ingredients

• Data

• Experience

• Learning Model

Data is cheap and abundant (data warehouses, data

marts); knowledge is expensive and scarce

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

• …

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

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?

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

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

?

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

Important Concepts (1)

Attributes/Features

Samples/Instances/Examples

Target Variable

Important Concepts (2)