Machine Learning

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 12.00 in B23 Mech Eng • Exercise sheets given out at lecture • Solutions discussed during tutorials • Handouts are on the module’s web page: http://www.cs.bham.ac.uk/~durranrj/ML.html

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. 15.00 – 16.00. • 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 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 • Supervisedlearning: uses a series of examples with direct feedback • Reinforcementlearning: indirect feedback, after many examples • Unsupervisedlearning: no feedback