Transcript vinci code
Introduction to Machine Learning Yazd University, Electrical and Computer Engineering Department Course Title: Advanced Software Engineering By: Mohammad Ali Zare Chahooki 1 Why “Learn” ? Machine learning is programming computers to … optimize a performance criterion using … example data or past experience. There is no need to “learn” to calculate payroll Learning is used when: Human expertise does not exist (navigating on Mars), Humans are unable to explain their expertise (speech recognition) Solution changes in time (routing on a computer network) Solution needs to be adapted to particular cases (user biometrics) 2 In “Learning” … Learning general models from a data of particular examples Data is cheap and abundant (data warehouses); knowledge is expensive and scarce. Example in retail … People who bought “DaVinci Code” also bought “The Five PeopleYou Meet in Heaven” (www.amazon.com) Build a model that is a good and useful approximation to the data. 3 What is Machine Learning? Optimize a performance criterion using example data or past experience. Role of Statistics: Inference from a sample Role of Computer science: Efficient algorithms to Solve the optimization problem Representing and evaluating the model for inference 4 Applications Learning Associations Supervised Learning Classification Regression Unsupervised Learning Reinforcement Learning 5 Learning Associations Basket analysis: P (Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services. Example: P ( chips | delester) = 0.7 6 Classification Example: Credit scoring Differentiating between low-risk and high-risk customers from their income and savings 7 Discriminant: IF income > θ1 AND savings > θ2 THEN low-risk ELSE high-risk Classification: Applications Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style Character recognition: Different handwriting styles. Speech recognition: Temporal dependency. Use of a dictionary or the syntax of the language. Sensor fusion: Combine multiple modalities; eg, visual (lip image) and acoustic for speech Medical diagnosis: From symptoms to illnesses ... 8 Face Recognition Training examples of a person Test images AT&T Laboratories, Cambridge UK http://www.uk.research.att.com/facedatabase.html 9 Regression Example: Price of a used car x : car attributes y : price y = g (x | θ ) g ( ) model, θ parameters 10 y = wx+w0 Regression Applications Navigating a car: Angle of the steering wheel (CMU NavLab) Kinematics of a robot arm (x,y) α2 α1 11 α1= g1(x,y) α2= g2(x,y) Supervised Learning: Uses Prediction of future cases: Use the rule to predict the output for future inputs Knowledge extraction: The rule is easy to understand Compression: The rule is simpler than the data it explains Outlier detection: Exceptions that are not covered by the rule, e.g., fraud 12 Unsupervised Learning In supervised learning, the aim is to … learn a mapping from the input to an output whose … correct values are provided by a supervisor. In unsupervised learning, there is no such supervisor and … we only have input data. The aim is to find the regularities in the input. One method is clustering where the aim is to find clusters or groupings of input. Other methods like feature reduction and finding association rules 13 Reinforcement Learning In some applications, the output of the system is a sequence of actions. In such a case, a single action is not important; what is important is the policy that is the sequence of correct actions to reach the goal. There is no such thing as the best action in any intermediate state; … an action is good if it is part of a good policy. 14 Reinforcement Learning In such a case, the machine learning program should be able to … assess the goodness of policies and … learn from past good action sequences to be able to generate a policy. Such learning methods are called reinforcement learning algorithms. A good example is game playing where … a single move by itself is not that important; it is the sequence of right moves that is good 15 Reference E. Alpaydın, "Introduction to Machine Learning 2nd edition Ed.“, MIT Press, (2010) E. Alpaydın, "Introduction to Machine Learning 3nd edition Ed.“, MIT Press, (2014) 16