Machine Learning Introduction

MS Electrical Engineering

COMSATS Institute of Information Technology

Wah Campus

Dr. Sajid Siraj

Fall 2012

Books

Machine Learning in Action

◦ Peter Harrington

2012

Manning Publications Co.

Machine Learning

◦ Tom M. Mitchell

1997

McGraw Hill

Machine Learning Journals

Machine Learning

Journal of Machine Learning Research

Neural Computation

Journal of Intelligent Systems

Machine Learning Journals

1. IEEE transactions on pattern analysis and machine intelligence Impact = 3.579

2. Artificial intelligence Impact = 3.008

3. IEEE transactions on neural networks Impact = 2.769

4. Journal of machine learning research Impact = 2.682

5. Neural computation Impact = 2.335

6. Pattern recognition Impact = 2.019

7. Neural networks Impact = 1.951

8. Artificial intelligence in medicine Impact = 1.825

9. Machine learning Impact = 1.742

10. Computer vision and image understanding Impact = 1.417

11. International journal of robotics research Impact = 1.318

12. Journal of artificial intelligence research Impact = 1.107

13. Journal of field robotics Impact = 0.960

14. Neurocomputing Impact = 0.865

15. Pattern recognition letters Impact = 0.853

16. Engineering applications of artificial intelligence Impact = 0.762

17. Applied artificial intelligence Impact = 0.753

18. Artificial intelligence review Impact = 0.634

19. Annals of mathematics and artificial intelligence Impact = 0.588

20. Pattern analysis and applications Impact = 0.515

21. Journal of experimental & theoretical artificial intelligence Impact = 0.500

22. International journal on artificial intelligence tools Impact = 0.376

23. International journal of pattern recognition and artificial intelligence Impact = 0.374

Conferences

International Conference on Machine

Learning (ICML)

Neural Information Processing Systems

(NIPS)

Course Outline

Week Topic Category

1 Machine Learning Introduction

2 K Nearest Neighbours Supervised Learning 3 Decision Trees Supervised Learning

4 Bayesian Learning Supervised Learning 5 Regression Supervised Learning

6 Support Vector Machines Supervised Learning 7 Neural Networks Supervised Learning 8 AdaBoost Supervised Learning

9 K-means Clustering Unsupervised Learning

10 A-priori Algorithm Unsupervised Learning

11 Self-organizing Maps Unsupervised Learning 12 Q Learning Reinforcement Learning 13 Multi-objective Optimization Optimization

14 Genetic Algorithms Optimization 15 Swarm Intelligence Optimization

16 Future of Machine Learning Conclusion

Motivation

◦ Imagine computers learning from medical

records Medical Decision Making

Prediction

Resource Allocation

Motivation

◦ Electricity usage pattern

Houses learning from experience to optimize

energy costs

Motivation

◦ Emails, Social Networking and Surfing

to highlight relevant stories from the newspaper.

Systems, Man & Cybernetics

Zoologists and psychologists study

learning in animals and humans.

◦ We focus on learning in machines

There are several parallels between animal and

machine learning

Many techniques in machine learning

derive from the efforts of psychologists

◦ It seems likely that researchers in machine

learning may

Unleash the aspects of biological learning

See IEEE Transaction on Systems, Man & Cybernetics

Understanding Humans

Machine learning might lead to a better

understanding of humans

◦ their learning abilities

and disabilities as well

Shifting Trends

In the last half of the twentieth century

world has moved from

◦ Manual labour “move this from here to there”

“put a hole in this”

to

◦ Knowledge work “maximize profits”

“minimize risk”

“find the best marketing strategy”

Knowledge Mining

We can’t afford to be lost in the data.

◦ Machine learning will help you get through all

the data

and extract some useful information.

Related Subjects

Machine Learning involves

◦ Computer Science

◦ Electrical Engineering

◦ Statistics

◦ Decision Sciences

Even from politics to geosciences

Any field that needs to interpret and act on data

can benefit from machine learning techniques

ML & Statistics

In engineering, we’re used to solving

◦ deterministic problems

where our solution solves the problem all the time.

There are many problems where the

solution isn’t deterministic For example, the motivation of students is a

problem that is too difficult to model.

◦ For these problems we need Statistics

ML & Brain Models

Simple models of biological neurons

◦ Non-linear elements with weighted inputs

Several machine learning techniques are

based on

◦ Networks of nonlinear elements often called

Neural networks

Also called Connectionism

or Sub-symbolic Processing

McCulloch, Pitts, Hebb, Rosenblatt, Rumelhart, Sejnowski, Koch, Churchland

ML & Adaptive Control Theory

Control theorists study the problem of

◦ Controlling a process having unknown

parameters

which must be estimated during operation

these parameters change during operation

the control process must track these changes

For example,

◦ Controlling a robot based on sensory inputs

For an introduction see B. Due

ML & Psychological Models

Psychologists have studied the

performance of humans in various

learning tasks.

◦ An early example is the EPAM network for storing and retrieving one member of a pair of words

when given another

(see Feigenbaum)

Related work led to the concept of

◦ Decision trees

Hunt, Marin, Stone

ML & Evolutionary Models

In nature, not only do individual animals learn to perform better

◦ Species also evolve to be better

Evolving through generations

Some examples:-

◦ Genetic Algorithms Holland, 1975

◦ Particle Swarm Optimization Kennedy, 1995

◦ Genetic programming Koza, 1990

ML Taxonomy

Supervised learning ◦ generates a function that maps inputs to desired outputs Classification (discrete domain)

Regression (continuous domain)

Unsupervised learning ◦ models a set of inputs

Reinforcement learning ◦ learns how to act given an observation of the world The environment provides feedback in the form of rewards that

guides the learning algorithm

Optimization ◦ Finding non-dominated solutions for problems in non-

convex domain.

Supervised Learning

Analyze the training data ◦ and produce an inferred function Classifier

if the output is discrete

Regression if the output is continuous

Algorithms:- ◦ kNN

◦ Decision Trees

◦ Bayesian Learning

◦ Logistic Regression

◦ Support Vector Machines

Unsupervised Learning

models a set of inputs

◦ like clustering

Algorithms:-

◦ K-Means Clustering

◦ Apriori Algorithm

◦ Self-organizing Maps

Optimization

Objectives? ◦ Single Objective Optimization

◦ Multi-objective Optimization

Domain? ◦ Convex

◦ Non-convex

Approaches? ◦ Classical Approaches Newton Method, Gradient descent, etc.

◦ Evolutionary Approaches Genetic Algorithms, Swarm Intelligence, etc.

Applications Machine Learning

Applications

Computer vision

Natural language

processing

Search engines

Medical diagnosis

Bioinformatics

Fraud detection

Stock market analysis

Speech recognition

Game playing

Robotics

Sentiment Analysis

Recommenders

Fraud Detection

◦ Credit Card Transactions, Fraud Detection,

and Machine Learning: Modelling Time with

LSTM Recurrent Neural Networks

B. Wiese and C. Omlin, 2009

Studies in Computational Intelligence, 247, 231-268

Speech Recognition

◦ Phoneme recognition using time-delay neural

networks

A. Waibel, 1989

IEEE Transactions on Acoustics, Speech and Signal

Processing, 37(3) 328 - 339

Medical Diagnosis

◦ An evaluation of machine-learning methods

for predicting pneumonia mortality

G. F. Cooper et al. 1997

Artificial Intelligence in Medicine, 9(2) 107-138

Cruise System

◦ Alvinn: An Autonomous Land Vehicle in a

Neural Network

D. A. Pomerleau, 2010

2010 IEEE International Conference on Robotics and

Automation (ICRA), 839 - 845

Cyber Assistant

A self-improving helpdesk service system

using case-based reasoning techniques K. H. Chang et al. 1996

Computers in Industry, 30(2),113-125

Power Load Forecasting

ALFA: automated load forecasting

assistant

◦ K. Jabbour et al.,1988

IEEE Transactions on Power Systems

3(3) 908 - 914