General Information

General Information Course Id: COSC6342 Machine Learning Time: Tuesdays and Thursdays 2:30 PM – 4:00 PM Professor: Ricardo Vilalta (vilalta@cs.uh.edu) Office: PGH 573 Telephone: (713) 743-3614 Office Hours: Tuesdays, Thursdays 1:30 PM – 2:30 PM

Textbook Textbook: “Machine Learning” by Tom Mitchell 1st Edition. Ed. McGraw-Hill, 1997 Additional Reading: “Pattern Classification” by Duda, Hart, and Stork 2nd Edition, Wiley-Interscience, 2000. “Computer Systems that Learn” by Kulikowski and Weiss.1st. Edition,1991.

Grading Midterm Exams 30% Homework 20% Project 20% Final Exam 30% NOTE: PLAGIARISM IS NOT TOLERATED.

Homework • Homework will include mainly exercises from the textbook • 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 • algorithm on a database or attempt a theoretical analysis. • The report must include a short survey of related work with the • corresponding list of references.

Dates to Remember September 30 1st Midterm Exam November 23 2nd Midterm Exam November 25 No class (Thanksgiving Holiday) December 2 Submit Project Report December 9 Final Exam (2:00-5:00 PM)

How to Succeed in 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. • The exams will cover the material covered in class only, but it is important to read the textbook thoroughly. • Assignments will prepare you well for the exam. • Exams should not be a problem if you have been following the classes and reading the textbook. • Familiarize with the software; think what aspect of machine learning you like the most soon.

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

Field of Study

Multidisciplinary Field Artificial Intelligence Neurobiology Probability & Statistics Machine Learning Computational Complexity Theory Philosophy Information Theory

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

Definition Machine learning is the study of how to make computers learn; the goal is to make computers improve their performance through experience. Computer Learning Algorithm PerformanceP Class of Tasks T Experience E

Class of Tasks Computer Learning Algorithm Class of Tasks T PerformanceP Experience E

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

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

Experience and Performance Computer Learning Algorithm Class of Tasks T PerformanceP Experience E

Experience and Performance Experience: What has been recorded in the past 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 Performance: Accuracy in classifications

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

Designing a Learning System Computer Learning Algorithm Class of Tasks T PerformanceP Experience E

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

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: X : the space of board configurations Y : the space of legal moves

The Representation of the Target Knowledge • Example: Diagnosing a patient coming into the hospital. • Features: • X1: Temperature • X2: Blood pressure • X3: Blood type • X4: Age • X5: Weight • Etc. Given a new example X = < x1, x2, …, xn > F(X) = w1x1 + w2x2 + w3x3 = … + wnxn If F(X) > T predict heart disease otherwise predict no heart disease

The Representation of the Target Knowledge • There are many possibilities: • 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. Don’t need many examples but may fail to contain the true target function.

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

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

Application 1

Application 1 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

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

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

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

Should I care about Machine Learning at all? • Yes, you should! • 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. • 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. • NASA is interested in robots able to adapt to any environment automatically.

Summary • 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. • 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.

General Information