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  1. 1. Machine Learning Bob Durrant School of Computer Science University of Birmingham (Slides: Dr Ata Kabán)
  2. 2. Machine Learning: The Module <ul><li>What is Learning ? </li></ul><ul><li>Decision trees </li></ul><ul><li>Instance-based learning </li></ul><ul><li>Kernel Machines </li></ul><ul><li>Probabilistic Models </li></ul><ul><li>Bayesian Learning </li></ul><ul><li>Learning Theory </li></ul><ul><li>Reinforcement Learning </li></ul><ul><li>Genetic Algorithms </li></ul>
  3. 3. Lectures & Tutorials <ul><li>Lectures on Monday at 14.00 in UG40 CS </li></ul><ul><li>Tutorials on Thursday at 12.00 in B23 Mech Eng </li></ul><ul><li>Exercise sheets given out at lecture </li></ul><ul><li>Solutions discussed during tutorials </li></ul><ul><li>Handouts are on the module’s web page: http://www.cs.bham.ac.uk/~durranrj/ML.html </li></ul>
  4. 4. Continuous Assessment <ul><li>ML: 20% of your final mark </li></ul><ul><li>ML-EXTENDED: 40% of your final mark </li></ul><ul><li>Two types of exercises </li></ul><ul><ul><li>Computer based practical work </li></ul></ul><ul><ul><ul><li>The exercises are posted on the module’s web page </li></ul></ul></ul><ul><ul><ul><li>Deadline: end of term </li></ul></ul></ul><ul><ul><li>Paper-based exercises (worksheets) </li></ul></ul><ul><ul><ul><li>The exercises are on the module’s web page & are handed out in lectures. </li></ul></ul></ul><ul><ul><ul><li>Deadline: before that week’s tutorial session. </li></ul></ul></ul>
  5. 5. Continuous Assessment (cont’d) <ul><li>Marking: </li></ul><ul><ul><li>There will be 12 pieces of assessed work provided during the course. </li></ul></ul><ul><ul><li>You must submit at least 6 pieces of work for ML, and at least 10 pieces of assessed work for MLX. </li></ul></ul><ul><ul><li>For MLX, you must submit Practical Assignments 1 and 2 (Assignment 1 counts as 3 pieces of assessed work). </li></ul></ul><ul><ul><li>Your assessed work score for ML (resp. MLX) will be the sum of your best 4 (or 8) pieces of submitted work. </li></ul></ul><ul><li>Feedback: </li></ul><ul><ul><li>You get immediate feedback on Worksheet exercises as we will solve them in the Thursday tutorial class. </li></ul></ul><ul><ul><li>You will also get your marked work returned to you (within 2 weeks). </li></ul></ul><ul><ul><li>You can approach me with questions in my office hours (as well as in tutorials, lectures, breaks). </li></ul></ul>
  6. 6. Office hours <ul><li>My weekly office hour follows straight after the Monday lecture, i.e. 15.00 – 16.00. </li></ul><ul><li>You are also welcome to approach me if you see me around campus. </li></ul><ul><li>Location: 134 (First Floor) </li></ul><ul><li>What office hours are and aren’t for: </li></ul><ul><ul><li>Yes: ask me concrete questions to clarify something that has not been clear to you from the lecture </li></ul></ul><ul><ul><li>Yes: seek advice on your solutions to the given exercises </li></ul></ul><ul><ul><li>Yes: seek advice on further readings on related material not covered in the lecture </li></ul></ul><ul><ul><li>No: ask me to solve the exercises </li></ul></ul><ul><ul><li>No: ask me to repeat a lecture </li></ul></ul>
  7. 7. Literature <ul><li>Machine Learning (Mitchell) </li></ul><ul><li>Reinforcement Learning … (Barto, Sutton) </li></ul><ul><li>Modelling the Web (Baldi, Smyth) </li></ul><ul><li>Support Vector Machines and Other Kernel-Based Learning Methods (Cristianini, Shawe-Taylor) </li></ul><ul><li>Artificial Intelligence … (Russell, Norvig) </li></ul><ul><li>Artificial Intelligence (Rich, Knight) </li></ul><ul><li>Artificial Intelligence (Winston) </li></ul><ul><li>Elements of Statistical Learning (Hastie, Tibshirani, Friedman) </li></ul><ul><li>Neural Networks: A Comprehensive Foundation (Haykin) </li></ul>
  8. 8. Module Web Page <ul><li>~durranrj </li></ul><ul><li>Syllabus </li></ul><ul><li>Handouts </li></ul><ul><li>Exercise sheets </li></ul><ul><li>Computer-based practical exercises </li></ul><ul><li>Links to ML resources on the web </li></ul><ul><li>Literature </li></ul>
  9. 9. What is Learning? How can Learning be measured? <ul><ul><li>Any change in the knowledge of a system that allows it to perform better on subsequent tasks. </li></ul></ul><ul><ul><li>Knowledge. How should knowledge be represented? Does anybody know how it is represented in the human brain? </li></ul></ul><ul><ul><li>Think for a moment about how knowledge might be represented in a computer. </li></ul></ul><ul><ul><li>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.) </li></ul></ul>
  10. 10. Ways humans learn things <ul><li>… talking, walking, running… </li></ul><ul><li>Learning by mimicking, reading or being told facts </li></ul><ul><li>Tutoring </li></ul><ul><li>Being informed when one is correct </li></ul><ul><li>Experience </li></ul><ul><li>Feedback from the environment </li></ul><ul><li>Analogy </li></ul><ul><li>Comparing certain features of existing knowledge to new problems </li></ul><ul><li>Self-reflection </li></ul><ul><li>Thinking things in one’s own mind, deduction, discovery </li></ul>
  11. 11. Machine Learning <ul><li>Interdisciplinary field </li></ul><ul><ul><li>Artificial intelligence </li></ul></ul><ul><ul><li>Bayesian methods </li></ul></ul><ul><ul><li>Computational complexity theory </li></ul></ul><ul><ul><li>Control theory </li></ul></ul><ul><ul><li>Information theory </li></ul></ul><ul><ul><li>Philosophy </li></ul></ul><ul><ul><li>Psychology and neurobiology </li></ul></ul><ul><ul><li>Statistics </li></ul></ul><ul><ul><li>… </li></ul></ul>
  12. 12. Achievements of ML <ul><li>Computer programs that can: </li></ul><ul><ul><li>Recognize spoken words </li></ul></ul><ul><ul><li>Predict recovery rates of pneumonia patients </li></ul></ul><ul><ul><li>Detect fraudulent use of credit cards </li></ul></ul><ul><ul><li>Drive autonomous vehicles </li></ul></ul><ul><ul><li>Play games like backgammon – approaching the human champion! </li></ul></ul>
  13. 13. What is the Learning problem? <ul><li>Learning = improving with experience at some task </li></ul><ul><ul><li>Improve over task T </li></ul></ul><ul><ul><li>With respect to performance measure P </li></ul></ul><ul><ul><li>Based on experience E </li></ul></ul><ul><li>Example: Learning to play checkers </li></ul><ul><ul><li>T: play checkers </li></ul></ul><ul><ul><li>P: % of games won in world tournament </li></ul></ul><ul><ul><li>E: opportunity to play against self </li></ul></ul>
  14. 14. <ul><li>Example: Learning to recognise faces </li></ul><ul><ul><li>T: recognise faces </li></ul></ul><ul><ul><li>P: % of correct recognitions </li></ul></ul><ul><ul><li>E: opportunity to make guesses and being told what the truth was </li></ul></ul><ul><li>Example: Learning to find clusters in data </li></ul><ul><ul><li>T: finding clusters </li></ul></ul><ul><ul><li>P: compactness of the groups detected </li></ul></ul><ul><ul><li>E: opportunity to see a large set of data </li></ul></ul>
  15. 15. Types of training experience <ul><li>Direct or indirect </li></ul><ul><li>With a teacher or without a teacher </li></ul><ul><li>An eternal problem: is the training experience representative of the performance goal? – It needs to be. </li></ul>
  16. 16. Forms of Machine Learning <ul><li>Supervised learning: uses a series of examples with direct feedback </li></ul><ul><li>Reinforcement learning: indirect feedback, after many examples </li></ul><ul><li>Unsupervised learning: no feedback </li></ul>

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