fundamentals of machine learning bootcamp - 24 nov london 2014

© 2014 Persontyle Ltd. All rights reserved.

FUNDAMENTALS OF

MACHINE LEARNING

BOOTCAMP

HANDS-ON INTRODUCTION TO MACHINE LEARNING MODELS, METHODS AND ALGORITHMS

www.persontyle.com© 2014 Persontyle Ltd. All rights reserved.

“THE FIELD OF MACHINE LEARNING IS

CONCERNED WITH THE QUESTION OF HOW TO

CONSTRUCT COMPUTER PROGRAMS THAT

AUTOMATICALLY IMPROVE WITH EXPERIENCE.”- TOM MITCHELL

MACHINE

LEARNING


Data generated through our activities captures plethora of information

about our identity, likes and dislikes etc. This information has tremendous

value in every aspect of human life. Programming computers to unravel this

hidden information is what Machine Learning is all about. It is the art and

science of scientifically deriving insights, patterns and predictions from data.

Though it has been an area of active research for over 50 years, Machine

Learning is currently undergoing a renaissance driven by Moore's law and

the rise of big data. Large private and public investment in the area has

given us self-driving cars, practical speech recognition, effective web search,

and a vastly improved understanding of the human genome. Computer

based Machine Learning algorithms now outperform humans on tasks such

as handwritten digit recognition, traffic sign recognition, and even on some

complex reasoning tasks as demonstrated by IBM's Watson winning

Jeopardy.

Machine Learning models and programs automatically make decisions from data inorder to achieve some goal or requirement. Machine learning models matter to theworld. Because they are;

# EFFICIENTMachine Learning models predict and detect partners faster than any other manualprogram or method.

# EFFECTIVEMachine Learning models can do better job than humans when analysing andpredicting large scale and streaming data sets (big data).

# SCALEMachine Learning models can provide solutions to large data problems thattraditional systems can not solve.


Machine perception

Computer vision,

including object recognition

Natural language processing

Pattern recognition

Search engines

Medical diagnosis

Bioinformatics

Brain-machine interfaces

Detecting credit card fraud

Stock market analysis

Classifying DNA sequences

Sentiment analysis

Affective computing

Information retrieval

Recommender systems

Examples in the real world include handwritten recognition,

weather prediction, fraud detection, search, facial recognition, and

so forth are all examples of machine learning in the wild.

Applications for Machine Learning include:

“Over the past two decades Machine Learning has become one of the

mainstays of information technology and with that, a rather central, albeit

usually hidden, part of our life. With the ever increasing amounts of data

becoming available there is good reason to believe that smart data analysis

will become even more pervasive as a necessary ingredient for

technological progress.”

DR. ALEXANDER J. SMOLA, PROFESSOR, CARNEGIE MELLON UNIVERSITY


Machine Learning enables computational systems to adaptively improve

their performance with experience accumulated from the observed data.

Though it has been an area of active research for over 50 years, Machine

Learning is currently undergoing a renaissance driven by Moore's law and

the rise of big data. Large private and public investment in the area has

given us self driving cars, practical speech recognition, effective web

search, and a vastly improved understanding of the human genome.

Computer based machine learning algorithms now outperform humans on

tasks such as handwritten digit recognition, traffic sign recognition, and

even on some complex reasoning tasks as demonstrated by IBM's Watson

winning Jeopardy.

Fundamentals of Machine Learning Bootcamp will take you through the

conceptual and applied foundations of the subject. Topics covered will

include Machine Learning theory, types of learning, techniques, models

and methods. Labs are developed to practically learn how to use the R

programming language and packages for applying the main concepts and

techniques of Machine Learning.

Over the course of five days, over two dozen techniques will be examined,

implemented through supervised exercises and tutorials, and compared.

You will learn the relative advantages and disadvantages of different types

of techniques in different contexts. You will see how some models are

entirely data driven, while others can be used to encode defeasible expert

knowledge. You will learn methods for validating selected models and

techniques and for choosing among alternative methods.

FUNDAMENTALS OF

MACHINE LEARNING

BOOTCAMP

WHAT WILL YOU LEARN?

In this bootcamp you will learn, among other things:

+ What Machine Learning entails and why it is

important

+ The different types of Learning, especially Supervised

Learning

+ Be able to use R to apply a number of the most

common and powerful statistical machine learning

techniques.

+ Know how to implement such techniques in principle

and therefore be able to apply their knowledge within

paradigms outside R.

+ Be able to appreciate the trade-offs involved in

choosing particular techniques for particular

problems.

+ Be able to utilize rigorous methods of model

selection.

+ Understand the mathematical ideas behind, and

relationships between, the various methods.

+ Have a greater confidence in their knowledge and

standing as a data scientist.

+ How to use these algorithms in a variety of

benchmark datasets

+ How to fine-tune these algorithms for better

performance


R logo is trademark of the R Foundation, from http://www.r-project.org

PREREQUISITES

Knowledge of R programming language and familiarity with linear algebra.

Basic familiarity with statistics and probability theory is recommended.

SCHEDULE AND LEARNING OBJECTIVES


Time Topic/Activity

09:00-09:30 Introduction

09:30-11:00 1. R Refresher

11:00-13:00 2. Linear and Quadratic Regression

After this module, you will:• Understand what regression is.• Understand what linearity is.• Understand the idea behind basis projection.• Be able to perform linear, quadratic and polynomial regression.• Be able to identify datasets that are suitable for linear and quadratic

regression.• Understand the idea of free parameters.

13:00-13:30 Lunch

13:30-15:00 2. Principle Component Analysis

After this module, you will:

• Understand how PCA functions.

• Understand how PCA can be used for feature selection and information

compression.

• Be able to perform PCA analysis and regression.

• Understand and be able to perform scaling and centring of data.

15:00 -15:15 Coffee Break

15:15-17:15 3. Feature Selection and Shrinkage


• Understand the idea of feature shrinkage

• Be able to use subset selection as a means of feature selection

• Be able to use Ridge Regression and the Lasso as means of feature

shrinkage.

• Understand what degrees of freedom are.

• Understand what the variance/bias trade-off is.

• Have a basic understanding of how both relate to the question of model

selection.

17:15-18:00 4. Error Estimation


• Know what residuals are

• Be able to model regression error using a normal distribution.

DAY 1 DAY 2 DAY 3 DAY 4 DAY 5



Time Topic/Activity

9:00-11:00 5. Real-Discrete Classification: LDA, QDA and Logistic Regression

After this module, you will:• Understand what classification tasks are, and the difference between real-

discrete and discrete-discrete classification.• Be able to apply LDA, QDA and Logistic Regression.

11:00-11:15 Coffee Break

11:15-13:00 6. Perceptron Classification

After this module you will:• Understand how to use the perceptron classifier in separable and inseparable

cases.• Understand the idea of linearly separable and inseparable data.• Understand the idea of iterative algorithms and termination conditions.

13:00-13:30 Lunch

13:30-15:30 6. Discrete-Discrete Classification & An Introduction to Bayesian Methods

After this module, you will:• Be able to apply conditional multinomial and noisy-or models to discrete-

discrete classification tasks.• Understand the idea behind Bayesian Methods in statistics• Be able to work with Dirichlet priors, and understand the idea of count and

pseudo-count parameters.


15:45-17:45 7. K-Means and Cluster Analysis

After this module, you will:• Understand and be able to compute the distance between data points.• Understand unsupervised learning and cluster analysis.• Be able to apply the K-Means and K-Mediod algorithms for cluster analysis.





Time Topic/Activity

9:00-11:00 8. K Nearest Neighbours

After this module, you will:• Understand what is meant by local methods, their weakness regarding memory

use, and the situations in which they are suitable• Be able to apply the K-Nearest-Neighbours and Adaptive K-Nearest-Neighbours

techniques


11:15-13:00 9. Local Regression

After this module, you will:• Be able to perform local regression.

13:00-13:30 Lunch

13:30-15:30 10. Kernel Density Estimation

After this module, you will:• Understand what a kernel is.• Be able to identify common kernels.• Understand what bandwidth is and why it is important.• Be able to perform kernel density estimation.• Understand what thinning is and be able to perform thinned kernel density

estimation using K-Means or K-Mediods.• Be able to identify cases where kernel density estimation is suitable.


15:45-18:00 11. Regression/Classification Trees and Boosting

After this module, you will:• Understand and be able to implement regression/classification trees.• Understand what boosting is.• Be able to implement the adaboost algorithm.




Time Topic/Activity

9:00-11:30 12- Splines

After this module, you will:• Understand what truncated exponential splines are and how we can use

bases projection to calculate them.• Understand the border issues associated with regression splines and how

natural splines assist in dealing with these. • Understand what B-Splines are and how they are used.• Be able to use truncated exponential regression and natural splines, as well

as B-Splines. • Be able to work with tensor products of such splines

11:30-13:00 13. MARS

After this module, you will:• Be able to use the MARS procedure for working with splines.• Be able to identify cases where such additive methods are appropriate.• Understand the idea of effective degrees of freedom.

13:00-13:30 Lunch

13:30-14:15 Azure Machine Learning Studio Overview – 1

14:15-16:30 14. Smoothing / Thin Plate Splines

After this module, you will:• Understand what smoothing splines are, their optimality guarantees and

their complexity issues.• Understand the connection between penalizing the second derivative of

smoothing splines and performing Ridge Regression on a transform of the dataset.

16:30-18:30 15. Radial Basis Networks

After this module, you will:• Understand what radial basis functions and networks are, how they make

use of kernels to project our data to new bases and the connection with ridge regression to smooth the resulting models.

• Be able to use Radial Basis Networks to model data.• Be able to use appropriate thinning strategies to avoid the complexity

issues identified.




Time Topic/Activity

09:15-10:15 16. Support Vector Classifiers

After this module, you will:• Know what support vectors, optimal hyperplanes and support vector

classifiers are.

10:15-12:15 17. Support Vector Machines

After this module, you will:• Understand how SVMs work, the reasons for their success, and the links

between them and simpler statistical models from earlier modules.• Be able to apply support vector machines to appropriate cases.

12:15-13:00 Azure Machine Learning Studio Overview – 2

13:00-13:30 Lunch Break

13:30-16:45 18. Neural Networks

After this module, you will:• Understand how Neural Networks work, the reasons for their success, and

the links between them and simpler statistical models from earlier modules.• Be able to train Neural Networks for classification and regression tasks using

the back-propagation algorithm with weight decay.• Be able to apply Neural Networks to appropriate cases.

16:45-18:15 19. Model Selection

After this module, you will:• Be able to apply validation and information criteria model selection methods

to real life problems.• Understand the advantages and disadvantages of the different methods.• Understand the relationship between model fitness and complexity measures

such as effective degrees of freedom.


Persontyle trainers are passionate about meeting each participants

learning needs. They have been chosen both for their extensive practical

Data Science and Machine Learning experience and for their ability to

educate and interact with natural empathy. All of our trainers have worked

on a variety of data science and Machine Learning projects. They share

their academic knowledge and real-world experience and each individual

adds their own unique perspective to the course. Our trainers present in a

style that is informal, entertaining and highly interactive.

Guest Speakers

Business leaders, Machine Learning practitioners, and academic

researchers covering use cases, case studies and sharing practical

experience of applying Data Science and Machine Learning in their

organizations.

COURSE INSTRUCTORS

“A BREAKTHROUGH IN MACHINE LEARNING

WOULD BE WORTH TEN MICROSOFTS” BILL GATES, CHAIRMAN, MICROSOFT

WHO SHOULD ATTEND

Anyone interested in learning and applying machine learning methods and

R to solve real-world data problems. Ideal for people interested in

pursuing career in data science.

This hands-on workshop is aimed at business and technology

professionals, Developer, Architect, Manager, Data Analyst, BI

Developer/Architect, QA, Performance Engineers, Sales, Pre Sales and

Marketing, Project Manager, Public Services, Teaching Staff and all

those who already have some basic competence in statistics but wish to

begin using R for machine learning for the first time.

For corporate bookings or to organize on-site training email

[email protected] or call now +44 (0)20 3239 3141

Register Now

RETURN ON INVESTMENT (ROI) CONVINCE YOUR BOSS

The advent of the data driven connected era means that analyzing massive

scale, messy, noisy, and unstructured data is going to increasingly form part

of everyone's work.

The School of Data Science learning programs provide a unique investment

opportunity that pay’s for itself many times over.

"For the best return on your money, pour your purse into your head."

World-class

Instructors

Benjamin Franklin

Develop Practical Data Science Skills

Real World Industry Use

Cases

Short Courses For Time

Convenience

Value For Money

THE SCHOOL OF DATA SCIENCE The School of Data Science, a project of Persontyle, specializes in designing and delivering

structured, relevant and practical learning experiences for all of us to understand data science in

simple human terms.

Follow us on Twitter @schooltds

Like us on Facebook

Get in touch! [email protected]

Limited seats. We encourage you to register as soon as you can.

WWW.PERSONTYLE.COM/SCHOOL

mailto:[email protected]

http://www.eventbrite.co.uk/e/fundamentals-of-machine-learning-bootcamp-london-registration-11263875555

https://twitter.com/schooltds

http://www.facebook.com/persontyle

mailto:[email protected]

fundamentals of machine learning bootcamp - 24 nov london 2014

Data & Analytics