1. Machine Learning Bob Durrant School of Computer Science
University of Birmingham
(Slides: Dr Ata Kabán)

2. Machine Learning: The Module
What is Learning?
Decision trees
Instance-based learning
Kernel Machines
Probabilistic Models
Bayesian Learning
Learning Theory
Reinforcement Learning
Genetic Algorithms

3. Lectures & Tutorials Lectures on Monday at 14.00 in UG40 CS
Tutorials on Thursday at in B23 Mech Eng
Exercise sheets given out at lecture
Solutions discussed during tutorials
Handouts are on the module’s web page:

4. Continuous Assessment
ML: 20% of your final mark
ML-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
Paper-based exercises (worksheets)
The exercises are on the module’s web page & are handed out in lectures.
Deadline: before that week’s tutorial session.

5. Continuous Assessment (cont’d)
Marking:
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.
Feedback:
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).

6. Office hours
My weekly office hour follows straight after the Monday lecture, i.e –
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 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

7. Literature Machine Learning (Mitchell)
Reinforcement Learning … (Barto, Sutton)
Modelling the Web (Baldi, Smyth)
Support Vector Machines and Other Kernel-Based Learning Methods (Cristianini, Shawe-Taylor)
Artificial Intelligence … (Russell, Norvig)
Artificial Intelligence (Rich, Knight)
Artificial Intelligence (Winston)
Elements of Statistical Learning (Hastie, Tibshirani, Friedman)
Neural Networks: A Comprehensive Foundation (Haykin)

8. Module Web Page ~durranrj Syllabus Handouts Exercise sheets
Computer-based practical exercises
Links to ML resources on the web
Literature

9. 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’ - something one should guard against.)

10. Ways humans learn things
…talking, walking, running…
Learning by mimicking, reading or being told facts
Tutoring
Being informed when one is correct
Experience
Feedback from the environment
Analogy
Comparing certain features of existing knowledge to new problems
Self-reflection
Thinking things in one’s own mind, deduction, discovery

11. Machine Learning Interdisciplinary field Artificial intelligence
Bayesian methods
Computational complexity theory
Control theory
Information theory
Philosophy
Psychology and neurobiology
Statistics …

12. 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!

13. 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

14. 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

15. 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.

16. Forms of Machine Learning
Supervised learning: uses a series of examples with direct feedback
Reinforcement learning: indirect feedback, after many examples
Unsupervised learning: no feedback