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Transcript

  • 1. Machine Learning Bob Durrant School of Computer Science University of Birmingham (Slides: Dr Ata Kabán)
  • 2. Machine Learning: The Module
    • What is Learning ?
    • Decision trees
    • Instance-based learning
    • Kernel Machines
    • Probabilistic Models
    • Bayesian Learning
    • Learning Theory
    • Reinforcement Learning
    • Genetic Algorithms
  • 3. 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
  • 4. 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.
  • 5. 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).
  • 6. 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
  • 7. 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)
  • 8. Module Web Page
    • ~durranrj
    • Syllabus
    • Handouts
    • Exercise sheets
    • Computer-based practical exercises
    • Links to ML resources on the web
    • Literature
  • 9. 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.)
  • 10. 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
  • 11. Machine Learning
    • Interdisciplinary field
      • Artificial intelligence
      • Bayesian methods
      • Computational complexity theory
      • Control theory
      • Information theory
      • Philosophy
      • Psychology and neurobiology
      • Statistics
  • 12. 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!
  • 13. 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
  • 14.
    • 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
  • 15. 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.
  • 16. 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

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