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

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7 s1s1 s2s2 s3s3 s4s4 x1x1 x2x2 x3x3 x4x4 a 11 a 12 a 13 a 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: 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