Algorithms 3 Algorithm: To solve a problem on a computer, we need an algorithm. An algorithm is a sequence of instructions that transforms a given input to output. For example, one can devise an algorithm for sorting. For sorting, there may be various algorithms and we may be interested in finding the most efficient one, requiring the least number of instructions (time) or memory or both.
Tasks without algorithms 4 For some tasks, however, we do not have an algorithm for example, to tell spam s from legitimate s. We know what the input is: an document that in the simplest case is a file of characters. We know what the output should be: a yes/no output indicating whether the message is spam or not. We do not know how to transform the input to the output. What can be considered spam changes in time and from individual to individual.
Example Data 5 We can easily compile thousands of example messages some of which we know to be spam and what we want is to learn what consititutes spam from them. In other words, we would like the computer (machine) to extract automatically (learn) an algorithm (model) for this task. There are many applications for which we do not have an algorithm but do have example data.
Machine Learning 6 Textbook definition: Machine learning is programming computers to optimize a performance criterion using example data or past experience. Tom Mitchells definition:
What is learning 7 H. Simon: Any process by which a system improves its performance M. Minsky: Learning is making useful changes in our minds R. Michalsky: Learning is constructing or modifying representations of what is being experienced L. Valiant: Learning is the process of knowledge acquisition in the absence of explicit programming
Why Learn ? 8 There is no need to learn to calculate payroll Learning is used when: Human expertise does not exist (navigating on 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)
What We Talk About When We Talk AboutLearning 9 Learning general models from a data of particular examples Data is cheap and abundant (data warehouses, data marts); knowledge is expensive and scarce. Example in retail: Customer transactions to consumer behavior: People who bought Blink also bought Outliers (www.amazon.com) Build a model that is a good and useful approximation to the data.
Data Mining 10 Application of machine learning methods to large databases is called data mining. Retail: Market basket analysis, Customer relationship management (CRM) Finance: Credit scoring, fraud detection Manufacturing: Control, robotics, troubleshooting Medicine: Medical diagnosis Telecommunications: Spam filters, intrusion detection Bioinformatics: Motifs, alignment Web mining: Search engines...
What is Machine Learning? 11 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
What is Machine Learning? Source: Tom Mithcell 12
Brief History 13 Studied ever since computers were invented (e.g. Samuel's checkers player) Very active in 1960s (neural networks) Died down in the 1970s Revival in early 1980s (decision trees, backpropagation, temporal difference learning) - coined as machine learning Exploded starting in the 1990s Now: very active research field, several yearly conferences (e.g., ICML, NIPS), major journals (e.g., Machine Learning, Journal of Machine Learning Research)
ML niche is growing 14 Machine learning already the preferred approach to Speech recognition, Natural language processing Computer vision Medical outcomes analysis Robot control The time is right to study in the field! Lots of recent progress in algorithms and theory Flood of data to be analyzed ( Increased data capture, networking, new sensors) Software too complex to write by hand Computational power is available Growing demand for industrial applications
ML Methods and Applications 15 Association Supervised Learning Classification Regression Unsupervised Learning Reinforcement Learning
Learning Associations 17 Basket analysis: P (Y | X ) probability that somebody who buys X also buys Y where X and Y are products/services. Example: P ( chips | beer ) = 0.7
Applications of Association Rule Mining 18 Market-basket analysis helps in cross-selling products in a sales environment. Web usage mining: Items can be links on Web pages; Is a person more likely to click on a link having clicked on another link? We may download page corresponding to the next likely link to be clicked. If a customer has bought certain products on Amazon. com, what else could he/she be temped to buy? Intrusion detection: Each (or chosen) action is an item. If one action or a sequence of actions has been performed, is the person more likely to perform a few other instructions that may lead to intrusion?
Classification 19 Example: Credit scoring We need a boundry (discriminant) differentiating between low-risk and high-risk customers from their income and savings Discriminant: IF income > θ 1 AND savings > θ 2 THEN low-risk ELSE high-risk
Classification: Applications 20 Aka Pattern recognition Face recognition: Pose, lighting, occlusion (glasses, beard), make-up, hair style Character recognition: Different handwriting styles. Speech recognition: Temporal dependency. Medical diagnosis: From symptoms to illnesses Biometrics: Recognition/authentication using physical and/or behavioral characteristics: Face, iris, signature, etc...
Regression Example: Price of a used car x : car attributes y : price y = g (x | ) g ( ) model, parameters 27 y = wx+w 0
Example: Navigation of a mobile robot or an autonomous car The output is the angle by which the steering wheel should be turned each time to advance without hitting obstacles and deviating from the route. The inputs are obtained from sensors on the car: video camera, GPS, etc. Training data is collected by monitoring the actions of a human driver. 28
Regression Applications 29 Navigating a car: Angle of the steering Kinematics of a robot arm α 1 = g 1 (x,y) α 2 = g 2 (x,y) α1α1 α2α2 (x,y)(x,y) Response surface design
Supervised Learning: Uses 30 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
Unsupervised Learning 32 Learning what normally happens No output Clustering: Grouping similar instances Example applications Customer segmentation in CRM Image compression: Color quantization Bioinformatics: Learning motifs
Resources: Journals 37 Journal of Machine Learning Research Machine Learning Neural Computation Neural Networks IEEE Transactions on Neural Networks IEEE Transactions on Pattern Analysis and Machine Intelligence Annals of Statistics Journal of the American Statistical Association...
Resources: Conferences 38 International Conference on Machine Learning (ICML) European Conference on Machine Learning (ECML) Neural Information Processing Systems (NIPS) Uncertainty in Artificial Intelligence (UAI) Computational Learning Theory (COLT) International Conference on Artificial Neural Networks (ICANN) International Conference on AI & Statistics (AISTATS) International Conference on Pattern Recognition (ICPR)...