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General Information Course Id: COSC6342 Machine Learning Time: Tuesdays and Thursdays 2:30 PM – 4:00 PM Professor: Ricardo Vilalta

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Presentation on theme: "General Information Course Id: COSC6342 Machine Learning Time: Tuesdays and Thursdays 2:30 PM – 4:00 PM Professor: Ricardo Vilalta"— Presentation transcript:

1 General Information Course Id: COSC6342 Machine Learning Time: Tuesdays and Thursdays 2:30 PM – 4:00 PM Professor: Ricardo Vilalta Office:PGH 573 Telephone: (713) Office Hours:Tuesdays, Thursdays 1:30 PM – 2:30 PM

2 Textbook Textbook: Machine Learning by Tom Mitchell 1 st Edition. Ed. McGraw-Hill, st Edition. Ed. McGraw-Hill, 1997 Additional Reading: Additional Reading: Pattern Classification by Duda, Hart, and Stork 2 nd Edition, Wiley-Interscience, nd Edition, Wiley-Interscience, Computer Systems that Learn by Kulikowski and Weiss.1 st. Edition,1991. by Kulikowski and Weiss.1 st. Edition,1991.

3 Grading Midterm Exams30% Homework20% Project 20% Final Exam30% NOTE: PLAGIARISM IS NOT TOLERATED.

4 Homework Homework will include mainly exercises from the textbook Homework will include mainly exercises from the textbook The project will be a report on some area in machine learning you The project will be a report on some area in machine learning you find most interesting. You can either report on some novel experiments after applying an You can either report on some novel experiments after applying an algorithm on a database or attempt a theoretical analysis. The report must include a short survey of related work with the The report must include a short survey of related work with the corresponding list of references.

5 Dates to Remember September 30 1 st Midterm Exam November 232 nd Midterm Exam November 25 No class (Thanksgiving Holiday) December 2Submit Project Report December 9 Final Exam (2:00-5:00 PM)

6 How to Succeed in Class In case you miss a class, read the chapter corresponding to that class. In case you miss a class, read the chapter corresponding to that class. Consult the professor during his office hours if you have questions. Consult the professor during his office hours if you have questions. The exams will cover the material covered in class only, but it The exams will cover the material covered in class only, but it is important to read the textbook thoroughly. is important to read the textbook thoroughly. Assignments will prepare you well for the exam. Assignments will prepare you well for the exam. Exams should not be a problem if you have been following the classes Exams should not be a problem if you have been following the classes and reading the textbook. and reading the textbook. Familiarize with the software; think what aspect of machine learning Familiarize with the software; think what aspect of machine learning you like the most soon. you like the most soon.

7 What is Machine Learning? Where does machine learning fit in computer science?Where does machine learning fit in computer science? What is machine learning?What is machine learning? Where can machine learning be applied?Where can machine learning be applied? Should I care about machine learning at all?Should I care about machine learning at all?

8 Field of Study

9 Multidisciplinary Field MachineLearning Probability & Statistics ComputationalComplexityTheory InformationTheory Philosophy Neurobiology ArtificialIntelligence

10 What is Machine Learning? Where does machine learning fit in computer science?Where does machine learning fit in computer science? What is machine learning?What is machine learning? DefinitionDefinition Design of a learning systemDesign of a learning system Where can machine learning be applied?Where can machine learning be applied? Should I care about machine learning at all?Should I care about machine learning at all?

11 Definition Machine learning is the study of how to make computers learn; the goal is to make computers improve their performance through experience. Experience E ComputerLearningAlgorithm Class of Tasks T Performance P

12 Class of Tasks Experience E ComputerLearningAlgorithm Class of Tasks T Performance P

13 Class of Tasks It is the kind of activity on which the computer will learn to improve its performance. Examples: Learning to Play chess Recognizing Recognizing Images of HandwrittenWords Diagnosing Diagnosingpatients coming into the e hospital

14 Settings for learning 1.Tasks are generated by a random process outside the learner 2.The learner can pose queries to a teacher 3.The learner explores its surroundings autonomously Example: Learning to play chess 1.Learn from a specific sequence 2.Ask: what if the sequence is this? 3.Give me an amateur player and then an expert player.

15 Experience and Performance Experience E ComputerLearningAlgorithm Class of Tasks T Performance P

16 Experience and Performance Experience: What has been recorded in the past Experience: What has been recorded in the past Performance: A measure of the quality of the response or action. Performance: A measure of the quality of the response or action. Example: Handwritten recognition using Neural Networks Experience: a database of handwritten images with their correct classification with their correct classification Performance: Accuracy in classifications

17 What is Machine Learning? Where does machine learning fit in computer science?Where does machine learning fit in computer science? What is machine learning?What is machine learning? DefinitionDefinition Design of a learning systemDesign of a learning system Where can machine learning be applied?Where can machine learning be applied? Should I care about machine learning at all?Should I care about machine learning at all?

18 Designing a Learning System Experience E ComputerLearningAlgorithm Class of Tasks T Performance P

19 Designing a Learning System 1.Define the knowledge to learn 2.Define the representation of the target knowledge 3.Define the learning mechanism Example: Handwritten recognition using Neural Networks 1.A function to classify handwritten images 2.A linear combination of handwritten features 3.A linear classifier

20 The Knowledge To Learn Supervised learning: A function to predict the class of new examples Let X be the space of possible examples Let Y be the space of possible classes Learn F : X Y Example: In learning to play chess the following are possible interpretations: In learning to play chess the following are possible interpretations: X : the space of board configurations X : the space of board configurations Y : the space of legal moves Y : the space of legal moves

21 The Representation of the Target Knowledge Example: Diagnosing a patient coming into the hospital. Features: X1: Temperature X1: Temperature X2: Blood pressure X2: Blood pressure X3: Blood type X3: Blood type X4: Age X4: Age X5: Weight X5: Weight Etc. Etc. Given a new example X = Given a new example X = F(X) = w1x1 + w2x2 + w3x3 = … + wnxn If F(X) > T predict heart disease otherwise predict no heart disease

22 The Representation of the Target Knowledge There are many possibilities: The class of functions is very expressive. The class of functions is very expressive. You can represent almost any function but to be effective the method needs lots of examples. The class of functions is very limited. The class of functions is very limited. Dont need many examples but may fail to contain the true target function.

23 The Learning Mechanism 1 Machine learning algorithms abound: Decision Trees Decision Trees Rule-based systems Rule-based systems Neural networks Neural networks Nearest-neighbor Nearest-neighbor Support-Vector Machines Support-Vector Machines Bayesian Methods Bayesian Methods Important characteristics of the learning mechanism: What is the class of functions What is the class of functions How do you search over the class of functions How do you search over the class of functions

24 The Learning Mechanism 2 Example: Look over the space of all possible decision trees. Prefer small trees to large trees. Higher score Lower score

25 What is Machine Learning? Where does machine learning fit in computer science?Where does machine learning fit in computer science? What is machine learning?What is machine learning? Where can machine learning be applied?Where can machine learning be applied? Should I care about machine learning at all?Should I care about machine learning at all?

26 Application 1

27 Automatic Car Drive Class of Tasks: Learning to drive on highways from vision stereos. Knowledge: Images and steering commands recorded while observing a human driver. Performance Module: Accuracy in classification

28 Application 2 Learning to classify astronomical structures. galaxy stars Features: o Color o Size o Mass o Temperature o Luminosity unkown

29 Application 2 Classifying Astronomical Objects Class of Tasks: Learning to classify new objects. Knowledge: database of images with correct classification. Performance Module: Accuracy in classification

30 Other Applications Bio-Technology Bio-Technology Protein Folding Prediction Protein Folding Prediction Micro-array gene expression Micro-array gene expression Computer Systems Performance Prediction Computer Systems Performance Prediction Banking Applications Banking Applications Credit Applications Credit Applications Fraud Detection Fraud Detection Character Recognition (US Postal Service) Character Recognition (US Postal Service) Web Applications Web Applications Document Classification Document Classification Learning User Preferences Learning User Preferences

31 What is Machine Learning? Where does machine learning fit in computer science?Where does machine learning fit in computer science? What is machine learning?What is machine learning? Where can machine learning be applied?Where can machine learning be applied? Should I care about machine learning at all?Should I care about machine learning at all?

32 Should I care about Machine Learning at all? Yes, you should! Yes, you should! Machine learning is becoming increasingly popular and has become a cornerstone in many industrial applications. Machine learning is becoming increasingly popular and has become a cornerstone in many industrial applications. Machine learning provides algorithms for data mining, where the goal is to extract useful pieces of information (i.e., patterns) from large databases. Machine learning provides algorithms for data mining, where the goal is to extract useful pieces of information (i.e., patterns) from large databases. The computer industry is heading towards systems that will be able to adapt and heal themselves automatically. The computer industry is heading towards systems that will be able to adapt and heal themselves automatically. The electronic game industry is now focusing on games where characters adapt and learn through time. The electronic game industry is now focusing on games where characters adapt and learn through time. NASA is interested in robots able to adapt to any environment automatically. NASA is interested in robots able to adapt to any environment automatically.

33 Summary Machine learning is the study of how to make computers learn. Machine learning is the study of how to make computers learn. A learning algorithm needs the following elements: class of tasks, performance metric, and body of experience. A learning algorithm needs the following elements: class of tasks, performance metric, and body of experience. The design of a learning algorithm requires to define the knowledge to learn, the representation of the target knowledge, and the learning mechanism. The design of a learning algorithm requires to define the knowledge to learn, the representation of the target knowledge, and the learning mechanism. Machine learning counts with many successful applications and is becoming increasingly important in science and industry. Machine learning counts with many successful applications and is becoming increasingly important in science and industry.


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