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

Machine Learning: The Module What is Learning? Decision trees Instance-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: http://www.cs.bham.ac.uk/~durranrj/ML.html

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.

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).

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

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)

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

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.)

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

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

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? – It needs to be.

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