# Introduction to Machine Learning Yazd University Electrical and

• Slides: 16

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 2 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)

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 “Da Vinci Code” also bought “The Five People You 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 > θ 1 AND savings > θ 2 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+w 0

Regression Applications �Navigating a car: Angle of the steering wheel (CMU Nav. Lab) �Kinematics of a robot arm (x, y) α 2 α 1 11 α 1= g 1(x, y) α 2= g 2(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 13 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

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 15 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

Reference �E. Alpaydın, "Introduction to Machine Learning 2 nd edition Ed. “, MIT Press, (2010) �E. Alpaydın, "Introduction to Machine Learning 3 nd edition Ed. “, MIT Press, (2014) 16