1

Machine Learning (Extended)Dr. Ata Kaban

• Algorithms to enable computers to learn – Learning = ability to improve performance automatically

through experience– Experience = previously seen examples

• Interdisciplinary field– AI– Probability & Statistics– Information theory– Philosophy– Control theory– Psychology– Neurobiology, etc

2

What is the Learning Problem?

3

Example: Which word a person is thinking about?

FMRI brain activity data:

Source: Tom Mitchell's research pages

4

Example: Find a specified object

5

6

1

2 3 4

5 6 7 8 910

11

9000

1800

2700 4500

810880 720

880

648

600

2128

282

2390 1800 2400

180023902400

72

University of Ulster

Department

InformationStudent

CSScience

&Arts

InternationalOffice Library

Under-graduate

Graduate

Jobs

200 300S

E

9000

Start

Exit

122128

Register

7

s

1

s

2

s

3

s

4

x

1

x

2

x

3

x

4

a

11

a

12

a

13a

14

8

What is the Learning problem?

Learning = improving with experience at some task– Improve at task T

– With respect to performance measure P

– Based on experience E

• Example: Learning to play checkers– T: play checkers

– P: % of games won in world tournament

– E: opportunity to play against self

9

• Example: Learning to recognise faces– T: recognise faces– P: % of correct recognitions– E: opportunity to make guesses and being told

what the truth was

• Example: Learning to find clusters in data– T: finding clusters– P: compactness of the groups detected– E: opportunity to see a large set of data

10

Types of training experience

• Direct or indirect

• With a teacher or without a teacher

• An eternal problem: is the training experience representative of the performance goal? – it needs to be.

11

Forms of Machine Learning

• Supervised learning: uses a series of examples with direct feedback

• Unsupervised learning: no feedback

• Reinforcement learning: indirect feedback, after many examples

Q: For the examples given, can you distinguish which type of learning they belong to?

12

Focus of the module

• Understanding the fundamental principles– Types of ML tasks – General algorithms and how they work– Which method is good for what and why– What ML methods can and cannot do– Open research problems

• This module is NOT a course on teaching to use a particular software package

13

Prerequisites

Mathematical Techniques for Computer Science (or equivalent)

Introduction to AI (or equivalent)

14

Syllabus

1.Overview of machine learning. Basic notions, literature

2.Supervised learning Generative methods Discriminative methods Computational learning theory basics Boosting and ensemble methods 3.Unsupervised learning Clustering Learning for structure discovery 4.Reinforcement learning basics 5.Topics in learning from high dimensional data and large scale learning

15

Literature

• Machine Learning (Mitchell)• A first course in Machine Learning (Rogers &

Girolami)• Support Vector Machines and Other Kernel-Based

Learning Methods (Cristianini, Shawe-Taylor) • Modelling the Web (Baldi, Smyth)• Artificial Intelligence … (Russell, Norvig)

+math refreshers on the ML module's website

16

Some achievements of ML

• Programs that can:– Recognize spoken words– Predict recovery rates of pneumonia patients– Detect fraudulent use of credit cards– Drive autonomous vehicles– Play games like backgammon – approaching

the human champion!

Assessment

Machine Learning: 20% Coursework; 80% exam.

Coursework: “Type 1” (i.e. pen & paper) - one class test (15%) - one take-home test (5%)

Assessment

Machine Learning Extended: 40% Coursework; 60% exam.

– All of the previous– “Type 2” (i.e. computer based problems) These consist of 4 pieces of work, handed out throughout

the term, with deadline at the end of term.

Classes, web site

• 2 hours / week• Some are lectures and some are exercise classes.• Module home page: http://www.cs.bham.ac.uk/~axk/ML_new.htm• The content currently there is from last year, and the page will be

updated as we go along. However it gives you a good idea of what to expect in terms of content, level of difficulty, types of assignments etc.

• Contains some math refreshers you might find useful: Linear Algebra & Probability Theory for Machine Learning

Office hours• The time for my weekly office hours is communicated on

my timetable (watch for possible changes):

• Location: UG32• What office hours are and aren’t for

– Yes: ask me concrete questions to clarify something that has not been clear to you from the lecture

– Yes: seek advice on your solutions to the given exercises

– Yes: seek advice on further readings on related material not covered in the lecture

– No: ask me to solve the exercises– No: ask me to repeat a lecture

http://www.cs.bham.ac.uk/~axk/timetable.html