Machine Learning - University of Birmingham

journeycartAI and Robotics

Oct 15, 2013 (3 years and 5 months ago)


Machine Learning

Bob Durrant

School of Computer Science

University of Birmingham

(Slides: Dr Ata

Machine Learning: The Module

What is

Decision trees

based learning

Kernel Machines

Probabilistic Models

Bayesian Learning

Learning Theory

Reinforcement Learning

Genetic Algorithms

Lectures & Tutorials

Lectures on Monday at 14.00 in UG40 CS

Tutorials on Thursday at 12.00 in B23 Mech Eng

Exercise sheets given out at lecture

Solutions discussed during tutorials

Handouts are on the module’s web page:

Continuous Assessment

ML: 20% of your final mark

EXTENDED: 40% of your final mark

Two types of exercises

Computer based practical work

The exercises are posted on the module’s web page

Deadline: end of term

based exercises (worksheets)

The exercises are on the module’s web page & are handed out in

Deadline: before that week’s tutorial session.

Continuous Assessment (cont’d)


There will be 12 pieces of assessed work provided during the course.

You must submit at least 6 pieces of work for ML, and at least 10 pieces
of assessed work for MLX.

For MLX, you must submit Practical Assignments 1 and 2 (Assignment 1
counts as 3 pieces of assessed work).

Your assessed work score for ML (resp. MLX) will be the sum of your best
4 (or 8) pieces of submitted work.


You get immediate feedback on Worksheet exercises as we will solve
them in the Thursday tutorial class.

You will also get your marked work returned to you (within 2 weeks).

You can approach me with questions in my office hours (as well as in
tutorials, lectures, breaks).

Office hours

My weekly office hour follows straight after the Monday
lecture, i.e. 15.00


You are also welcome to approach me if you see me
around campus.

Location: 134 (First Floor)

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

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


Machine Learning (Mitchell)

Reinforcement Learning … (
, Sutton)

Modelling the Web (
, Smyth)

Support Vector Machines and Other Kernel
Based Learning
Methods (

Artificial Intelligence … (Russell,

Artificial Intelligence (Rich, Knight)

Artificial Intelligence (Winston)

Elements of Statistical Learning (Hastie,
, Friedman)

Neural Networks: A Comprehensive Foundation (

Module Web Page




Exercise sheets

based practical exercises

Links to ML resources on the web


What is Learning?

How can Learning be measured?

Any change in the knowledge of a system that allows it
to perform better on subsequent tasks.

Knowledge. How should knowledge be represented?
Does anybody know how it is represented in the
human brain?

Think for a moment about how knowledge might be
represented in a computer.

If I told you what subjects would come up in the exam,
you might do very well. Would you do so well if I then
set randomly chosen subjects from the syllabus? (This
illustrates the notion called ‘overfitting’

one should guard against.)

Ways humans learn things

…talking, walking, running…

Learning by mimicking, reading or being told facts


Being informed when one is correct


Feedback from the environment


Comparing certain features of existing knowledge to new


Thinking things in one’s own mind, deduction, discovery

Machine Learning

Interdisciplinary field

Artificial intelligence

Bayesian methods

Computational complexity theory

Control theory

Information theory


Psychology and neurobiology


Achievements of ML

Computer 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!

What is the Learning problem?

Learning = improving with experience at some task

Improve over 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

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

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?

needs to be.

Forms of Machine Learning


learning: uses a series of
examples with direct feedback


learning: indirect feedback,
after many examples


learning: no feedback