ml-chap1-Intro

Machine Learning XUELEI HU School of Computer Science & Technology Nanjing University of Science & Technology xlhu@mail.njust.edu.cn http://xueleihu.name Lecture Slides for 2Course Information „ Course home page: http://xueleihu.name/ml.html „ Lecturer: Xuelei Hu (xlhu@mail.njust.edu.cn, Rm. 720) 3Course Information „ Reference books: … Pattern Recognition and Machine Learning; Chris Bishop … The Elements of Statistical Learning: Data Mining, Inference, and Prediction; Trevor Hastie, Robert Tibshirani, Jerome Friedman … Tom M. Mitchell, Machine Learning, McGraw Hill, 1997. … Ethem Alpaydin, Introduction to Machine Learning MIT Press, 2004. „ Grading: Assignments (50%), Final Project (50%) … Start early and do not plagiarize! 4Enjoy☺ „ ML is becoming ubiquitous in science, engineering and beyond „ This class should give you the basic foundation for applying ML and developing new methods „ The fun begins… CHAPTER 1: Introduction 6What is Machine Learning ? „ Study of algorithms that … improve their performance … at some task … with experience Data Understanding 7What is Machine Learning? „ 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 tasks in T, as measured by P, improves with experience E. ----Tom M. Mitchell „ Machine learning is programming computers to optimize a performance criterion using example data or past experience. ----Ethem Alpaydin 8Why is Machine Learning Important? „ Human expertise does not exist (e.g., navigating on Mars). „ Some tasks cannot be defined well, except by examples (e.g., recognizing people). „ Relationships and correlations can be hidden within large amounts of data (e.g., market basket analysis) „ Environments change over time. (e.g., routing on a computer network) „ …… 9Examples of Successful Applications „ Learning to recognize spoken words (Lee, 1989; Waibel, 1989). „ Learning to drive an autonomous vehicle (Pomerleau, 1989). „ Learning to classify new astronomical structures (Fayyad et al., 1995). „ Learning to play world-class backgammon (Tesauro 1992, 1995). Classification „ from data to discrete classes Spam filtering „ Data Prediction Text classification Company home page vs Personal home page vs Univeristy home page vs … Object detection (Prof. H. Schneiderman) Example training images for each orientation Weather prediction The classification pipeline „ Training „ Testing Regression „ predicting a numeric value Stock market 18 Weather prediction revisted Temperature 19 Similarity „ finding data 20 Given image, find similar images „ http://www.tiltomo.com Similar products 21 22 Clustering „ discovering structure in data Clustering Data: Group similar things 23 Clustering images 24 Clustering web search results 25 Embedding „ visualizing data 26 Embedding images Images have thousands or millions of pixels. Can we give each image a coordinate, such that similar images are near each other? 27 [Saul & Roweis ‘03] Embedding words „ [Joseph Turian] 28 Reinforcement Learning „ training by feedback 29 Learning to act „ Reinforcement learning „ An agent … Makes sensor observations … Must select action … Receives rewards … positive for “good” states … negative for “bad” states 30 [Ng et al. ’05] Bringing it all together… 31 Combining video, text and audio 32 Taskar et al. 33 Types of Learning Problems „ Unsupervised Learning, where we are interested in capturing inherent organization in the data … Clustering … Density estimation „ Supervised Learning, where we get a set of training inputs and outputs … Classification … Regression „ Reinforcement Learning, where we only get feedback in the form of how well we are doing (not what we should be doing) … Planning „ Semi-supervised Learning, where we get a set of labeled data and unlabeled data „ Active Learning, where we can query for feedback Growth of Machine Learning „ Machine learning is preferred approach to … Speech recognition, Natural language processing … Computer vision … Medical outcomes analysis … Robot control … Computational biology … Sensor networks … … „ This trend is accelerating … Improved machine learning algorithms … Improved data capture, networking, faster computers … Software too complex to write by hand … New sensors / IO devices … Demand for self-customization to user, environment 34 Prerequisites „ Probabilities … Distributions, densities, marginalization… „ Basic statistics … Moments, typical distributions, regression… „ Algorithms … Dynamic programming, basic data structures, complexity… „ Programming … Mostly your choice of language, but Matlab will be very useful „ We provide some background, but the class will be fast paced „ Ability to deal with “abstract mathematical concepts” 35 36 Relevant Areas „ Statistics: How best to use samples drawn from unknown probability distributions to help decide from which distribution some new sample is drawn? „ Brain Models: Non-linear elements with weighted inputs (Artificial Neural Networks) have been suggested as simple models of biological neurons. „ Adaptive Control Theory: How to deal with controlling a process having unknown parameters that must be estimated during operation? 37 Relevant Areas (Cont’d) „ Psychology: How to model human performance on various learning tasks? „ Artificial Intelligence: How to write algorithms to acquire the knowledge humans are able to acquire, at least, as well as humans? „ Evolutionary Models: How to model certain aspects of biological evolution to improve the performance of computer programs? „ …… 38 Resources: Journals „ Journal of Machine Learning Research www.jmlr.org „ Machine Learning „ Neural Computation „ IEEE Transactions on Neural Networks „ IEEE Transactions on Pattern Analysis and Machine Intelligence „ Annals of Statistics „ Journal of the American Statistical Association „ ...... 39 Resources: Conferences „ International Conference on Machine Learning (ICML) „ Neural Information Processing Systems (NIPS) „ European Conference on Machine Learning (ECML) „ Uncertainty in Artificial Intelligence (UAI) „ Computational Learning Theory (COLT) „ International Joint Conference on Artificial Intelligence (IJCAI) „ International Conference on Neural Networks (Europe) „ ......