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

What is the Learning Problem?

Example: Which word a person is thinking about? FMRI brain activity data: Source: Tom Mitchell's research pages

Example: Find a specified object

s 1 s 2 s 3 s 4 x 1 x 2 x 3 x 4 a 11 a 12 a 13 a 14

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

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? – it needs to be.

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?

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!

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

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

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

Machine Learning & Machine Learning Extended -- Administratrivia -- Ata Kaban 2014/15

Assessment Machine Learning Extended: 40% Coursework; 60% exam. Coursework = Assignments of Type 1 and Type 2: Type 1: Paper based problems. These will be assessed in one Class Test (15%) and one take-home assignment (5%). For each, the date and deadline date will be communicated one week in advance. Type 1: Computer based problems. These consist of 4 pieces of work, handed out throughout the term, with deadline at the end of term. Machine Learning: 20% Coursework; 80% exam. Coursework: Type 1 in the form of a class test (15%) For the remaining 5% you can choose between a Type 1 assignment or any of the four Type 2 assignments above. Non-assessed formative exercise sheets will be given out frequently. These are highly recommended for practice.

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