يادگيري ماشين Machine Learning Lecturer: A. Rabiee azrabiee@gmail.com Rabiee.iauda.ac.ir

منابع و مراجع Main Reference: - Mitchell, T. M. (1997). Machine learning. WCB. Other References: Haykin, S. S. (2009). Neural networks and learning machines (Vol. 3). Upper Saddle River: Pearson Education. Mitchell, T. M. (1999). Machine learning and data mining. Communications of the ACM, 42(11), 30-36. Anderson, J. R. (1986). Machine learning: An artificial intelligence approach(Vol. 2). R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.). Morgan Kaufmann. Mitchell, M. (1998). An introduction to genetic algorithms. MIT press. Witten, I. H., & Frank, E. (2011). Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann. Hagan, M. T., Demuth, H. B., & Beale, M. H. (1996). Neural network design(pp. 2-14). Boston: Pws Pub.. Kecman, V. (2001). Learning and soft computing: support vector machines, neural networks, and fuzzy logic models. MIT press. - …. http://coel.ecgf.uakron.edu/durkin/esbook.htm

Course Outline Chapter 1: Introduction Chapter 3: Decision tree learning Chapter 4: Artificial Neural Networks Chapter 9: Genetic Algorithms Chapter 13: Reinforcement Learning

ارزشيابي درس Final Exam: 50 Mini Projects (2 to 4): 20 Final Project + Presentation: 30 Paper (optional): +15

Introduction to Machine Learning Chapter 1: Introduction to Machine Learning

Table of Contents Definition & Examples Applications Why ML? ML Problems

Definition (Mitchell 1997) Machine Learning Learn from past experiences Improve the performances of intelligent programs Definition A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at the tasks improves with the experiences

Examples Text Classification (or spam classification) Task T Assigning texts to a set of predefined categories Performance measure P Precision of each category Training experiences E (Dataset) A dataset of texts with their corresponding categories How about Disease Diagnosis? How about Chess Playing?

Two phases Two phases of a learning process: Train Test

Example: Classification of texts based on content Text classifier New text file class Classified text files Text file 1 trade Text file 2 ship … … Training Phase 1: train Phase 2: test

Example: Heart disease diagnosis Disease classifier New patient’s data Presence or absence Database of medical records Patient 1’s data Absence Patient 2’s data Presence … … Training

Example: Chess Playing Strategy of Searching and Evaluating New matrix representing the current board Best move Games played: Game 1’s move list Win Game 2’s move list Lose … … Training

Machine Learning Problems Clustering: Grouping similar instances Dimension Reduction: Image Compression Regression: Tuning the angle of a robot arm

Application: Image Categorization (two phases) Training Labels Training Images Classifier Training Training Image Features Trained Classifier Image Features Testing Test Image Trained Classifier Outdoor Prediction

Feature Extraction Training Training Labels Classifier Training Training Images Classifier Training Training Image Features Trained Classifier

Example: Boundary Detection Is this a boundary?

Training Algorithm Training Training Labels Classifier Training Training Images Classifier Training Training Image Features Trained Classifier The main aim of this course

Classifier Training Example: A 2-class classifier Given some set of features with corresponding labels, learn a function to predict the labels from the features Example: Credit scoring Discriminant (model): IF income > θ1 AND savings > θ2 THEN low-risk ELSE high-risk

Different Learning Algorithms Decision Tree Learning Neural networks Naïve Bayes Genetic Algorithm K-nearest neighbor (clustering) Reinforcement Learning Support Vector Machine (SVM) …

Note The decision to use machine learning is more important than the choice of a particular learning method.

Why Machine Learning Is Possible? Mass Storage More data available Higher Performance of Computer Larger memory in handling the data Greater computational power for calculating and even online learning

Advantages Alleviate Knowledge Acquisition Bottleneck Adaptive Does not require knowledge engineers Scalable in constructing knowledge base Adaptive Adaptive to the changing conditions Easy in migrating to new domains

Success of Machine Learning Almost All the Learning Algorithms Text classification (Dumais et al. 1998) Gene or protein classification optionally with feature engineering (Bhaskar et al. 2006) Reinforcement Learning Backgammon (Tesauro 1995) Learning of Sequence Labeling Speech recognition (Lee 1989) Part-of-speech tagging (Church 1988)

Datasets UCI Repository: http://www.ics.uci.edu/~mlearn/MLRepository.html UCI KDD Archive: http://kdd.ics.uci.edu/summary.data.application.html Statlib: http://lib.stat.cmu.edu/ Delve: http://www.cs.utoronto.ca/~delve/ US government free data: data.gov US government free data (California): data.ca.gov …. for other states and the UK data.gov.uk, as well Stock market softwares Weather forecasting websites Reuters: data set for text classification ….

What I will Talk about Machine Learning Methods Method Details Simple methods Effective methods (state of the art) Method Details Ideas Assumptions Intuitive interpretations

What I won’t Talk about Machine Learning Methods Method Details Classical, but complex and not effective methods (e.g., complex neural networks) Methods not widely used Method Details Theoretical justification Theorem proving

What You will Learn Machine Learning Basics Others Methods Data Assumptions Ideas Others Problem solving techniques Extensive knowledge of modern techniques