311----introduction tomachinelearning.ppt
- 1. 3.1.1 Introduction to Machine Learning 2nd Edition Knowledge Component 3: Acquiring Data and Knowledge 1 Ian F. C. Smith EPFL, Switzerland
- 2. Module Information • Intended audience – Novice • Key words – Machine learning – Supervised learning – Unsupervised learning • Reviewer (1st Edition) – Ian Flood, U of Florida, Gainesville, USA 2
- 3. 3 What there is to learn At the end of this module, there will be answers to the following questions (see the quiz): •What are the different ways in which computers can learn? •Are there learning tasks that humans can do much better than computers?
- 4. Machine Learning Area of Influence Successful Applications Forms of Machine Learning Types of Learning Algorithms Outline 4
- 5. Humans learn from experience and adapt their actions for future tasks. Can machines adapt their behavior using experience? Since the 1950s, researchers have been trying to develop techniques that enable machines to learn. There have been much success in areas such as automatic control, recognition systems and natural language processing. Other successes are emerging. Machine Learning 5
- 6. An algorithm is said to learn from experience E with respect some class of tasks T and performance measure P … … if its performance at tasks in T, as measured by P, improves as it does task in T (experience E). What is Machine Learning ? 6
- 7. Example 1 Learning to recognize faces – T: recognize faces – P: % of correct recognitions – E: opportunity to makes guesses and being told what the truth is Example 2 Learning to find clusters in data – T: finding clusters – P: compactness of groups detected – E: analyses of a growing set of data Machine Learning - Examples 7
- 8. Existing machine learning techniques are applicable only when the learning task is well-defined. In many engineering applications, it is possible to formalize the learning task of specific “sub- problems”. Current Status 8
- 9. Machine Learning Area of Influence Successful Applications Forms of Machine Learning Types of Learning Algorithms Outline 9
- 10. Machine learning research is often interdisciplinary. There are synergies in the following fields: Statistics Brain models Adaptive Control Theory Psychology Artificial Intelligence Evolutionary models Information theory Philosophy Areas of influence 10
- 11. Machine Learning Area of Influence Successful Applications Forms of Machine Learning Types of Learning Algorithms Outline 11
- 12. Learning to predict risk of failures for components and systems of New York city power grid. (Rudin et al. 2012 ) Learning to analyze and predict the response of wind turbine structures to varying wind field characteristics. (Park et al. 2013) Learning to assess chlorine concentration in WDS. (Cuesta Cordoba et al., 2014) Successful Applications 12
- 13. Machine Learning Area of Influence Successful Applications Forms of Machine Learning Types of Learning Algorithms Outline 13
- 14. Supervised learning A series of examples are used for feedback Unsupervised learning No feedback Reinforcement learning Indirect feedback after experience Forms of Machine Learning 14
- 15. A learning task involves a set of input variables and a set of output variables. The set of possible relationships (hypotheses) between input and output variables is known as the hypothesis space. Hypothesis have representations such as numerical functions, symbolic rules, decision trees and artificial neural nets. Most learning is performed in a closed world where the hypothesis space is predefined and finite. Supervised Learning 15
- 16. The learning algorithm attempts to find the best hypothesis that maps input to output using “feedback”. Feedback consists of a set of points (training data) for which values of input and output variables are known. Since training data are used, this is supervised learning. Supervised Learning (cont'd.) 16
- 17. In unsupervised learning, output variables are not known. Unsupervised learning algorithms identify trends in data and make inferences without knowledge of correct answers. Unsupervised Learning 17
- 18. Reinforcement learning is concerned with how software ought to initiate actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms identify ways to maps states of the world to the actions the software ought to take in those states. Reinforcement learning may involve learning from mistakes. Reinforcement Learning 18
- 19. Machine Learning Area of Influence Successful Applications Forms of Machine Learning Types of Learning Algorithms Outline 19
- 20. There are four types of machine learning algorithms: Rote Statistical Deductive "Exploration and discovery“ These types are another way to classify machine learning and are mostly independent of the forms of machine learning defined earlier. The next module provides more detail. Types of Learning Algorithms 20
- 21. Give an example of a learning task that is easy for a human being but hard for a computer. Name the different forms of machine learning. What is the difference between supervised and unsupervised learning? Review Quiz I
- 22. Give an example of a learning task that is easy for a human being but hard for a computer. An example is image recognition. A 5-year old child is able to distinguish between a car and a tree in a picture. This task is difficult for a computer; it is hard to write a program that distinguishes the two. A machine learning algorithm could be used successfully to perform image recognition Answers to Review Quiz I
- 23. Name the different forms of machine learning. Supervised, unsupervised, reinforcement What is the difference between supervised and unsupervised learning? In case of supervised learning, there is a training set which contains input and output for a number of examples. The output is used as a feedback to learn from the data. In unsupervised learning, there is no training set. This kind of learning algorithms make inference from trends in data. Answers to Review Quiz - I
- 24. 24 Mitchell, T. Machine Learning. New York: McGraw- Hill, 1997 Kromanis et al.(2013). “Support vector regression for anomaly detection from measurement histories”. Dópido et al. (2013). ”Semisupervised self learning for hyperspectral image classification”. Raphael, B. and Smith, I.F.C. “Engineering informatics - fundamentals of computer-aided engineering”, Wiley, 2013. Further Reading
- 25. 25 Cuesta Cordoba et al. (2014). “Using artificial neural network models to assess water quality in water distribution networks”. Park et al. (2013). “Multivariate analysis and prediction of wind turbine response to varying wind field characteristics based on machine learning”. Rudin et al.(1998). “Machine learning for the New York city power grid”. Further Reading