Machine Learning Ata Kaban The University of Birmingham
Machine Learning: The Module <ul><li>What is  Learning ?  </li></ul><ul><li>Decision trees  </li></ul><ul><li>Instance-bas...
Lectures & Tutorials <ul><li>Lectures </li></ul><ul><li>Tutorial immediately following lecture. </li></ul><ul><li>Exercise...
Continuous Assessment <ul><li>ML: 20% of your final mark </li></ul><ul><li>ML-EXTENDED: 40% of your final mark </li></ul><...
Continuous Assessment (cont’d) <ul><li>Options and restrictions: </li></ul><ul><ul><li>You choose which pieces of your wor...
Office hours <ul><li>The time for my weekly office hours is communicated on my timetable  (watch for possible changes): </...
Literature <ul><li>Machine Learning (Mitchell) </li></ul><ul><li>Reinforcement Learning … (Barto, Sutton) </li></ul><ul><l...
Module Web Page <ul><li>~axk  </li></ul><ul><li>Syllabus  </li></ul><ul><li>Handouts </li></ul><ul><li>Exercise sheets </l...
<ul><li>What is Learning? </li></ul><ul><li>How can Learning be measured? </li></ul><ul><ul><li>Any change in the knowledg...
Ways humans learn things <ul><li>… talking, walking, running… </li></ul><ul><li>Learning by mimicking, reading or being  t...
Machine Learning <ul><li>Interdisciplinary field </li></ul><ul><ul><li>Artificial intelligence  </li></ul></ul><ul><ul><li...
Achievements of ML <ul><li>Computer programs that can: </li></ul><ul><ul><li>Recognize spoken words </li></ul></ul><ul><ul...
What is the Learning problem? <ul><li>Learning = improving with experience at some task </li></ul><ul><ul><li>Improve over...
<ul><li>Example: Learning to recognise faces </li></ul><ul><ul><li>T: recognise faces </li></ul></ul><ul><ul><li>P: % of c...
Types of training experience <ul><li>Direct or indirect </li></ul><ul><li>With a teacher or without a teacher </li></ul><u...
Forms of Machine Learning <ul><li>Supervised  learning: uses a series of examples with direct feedback </li></ul><ul><li>R...
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Basic Notions of Learning, Introduction to Learning ...

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Basic Notions of Learning, Introduction to Learning ...

  1. 1. Machine Learning Ata Kaban The University of Birmingham
  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 </li></ul><ul><li>Tutorial immediately following lecture. </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/~axk/ML_new.htm </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 the following weeks lecture </li></ul></ul></ul><ul><li>Many options, but: </li></ul>
  5. 5. Continuous Assessment (cont’d) <ul><li>Options and restrictions: </li></ul><ul><ul><li>You choose which pieces of your work you put forth for marking </li></ul></ul><ul><ul><li>Best strategy to give a try to every Worksheet exercise, hand in your solution before we solve it in the class, then decide whether you leave your solution with me for marking or you take it back. </li></ul></ul><ul><ul><li>You can only take it back before it gets marked </li></ul></ul><ul><ul><li>I mark the first 5 (9 for EXT) pieces of work that you put forth and take the best 4 (8 for EXT) marks. </li></ul></ul><ul><li>Feedback: </li></ul><ul><ul><li>You get immediate feedback on Worksheet exercises as we solve them in the class. </li></ul></ul><ul><ul><li>You will also get your marked work returned to you (in 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>The time for my weekly office hours is communicated on my timetable (watch for possible changes): </li></ul><ul><li>Location: UG32 </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>http://www.cs.bham.ac.uk/~axk/timetable.html
  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>
  8. 8. Module Web Page <ul><li>~axk </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. <ul><li>What is Learning? </li></ul><ul><li>How can Learning be measured? </li></ul><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. Hmmm. How should knowledge be represented. We do not know how it is represented in our own brains! </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 on 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 ones 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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