Machine Learning 09/10
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1 Machine Learning 09/10 R E G E N T : A L E X A N D R E B E R N A R D I N O Motivation Instead of pre-programming systems to perform tasks, let them learn from the sensory data, from observing others, from trial and error, from knowledge transfer,... Humans can learn from experience and examples. Human learning has been a source of inspiration in the design of learning algorithms. At the same time, the experience gathered in the design of machine learning methods highlights several aspects of learning in biological systems.
2 Outline This Year s Module Goals Organization Contents Bibliography A Brief Introduction to Machine Learning What is Machine Learning? What problems can be tackled by Machine Learning? Examples. What we know and not know on Machine Learning. Human vs Machine Learning Goals The course presents an introduction to Learning Theory: the study and modeling of systems that can learn from past experience and find useful patterns in the data. Laboratory sessions will allow the students to consolidate the learned concepts through computer simulations (Matlab).
3 Organization Lectures : Tuesdays and Thursdays, 15:30h-17:00h, EA3 Labs/Problem Sessions: Tuesdays or Thursdays, 17:00h-18:30h, LSDC1 Assessment: Final Exam (50%) Lab Work (50%) 5 out of 6 works are evaluated Web page Contents Mathematical and statistical models of learning systems: - Supervised and unsupervised learning. - Optimization methods - The problem of generalization. - The statistical view of machine learning. Computational Models: - Artificial Neural Networks - Support Vector Machines - Decision Trees - Clustering and Vector Quantization - Principal Component Analysis Biological Aspects: - Biological Motivation - The human brain - Models of biological neurons: -McCulloch & Pitts Neuron -Rosenblatt s Perceptron -Widrow & Hoff Adaline
4 Bibliography Online Contents Slides Handouts Essential and Auxiliary Papers Problems, Past Exams Books [Mitchell99] Machine Learning, Tom Mitchell, McGraw-Hill, [Haykin95] Neural Networks, Simon Haykin, MacMillan, [Marques05] Reconhecimento de Padrões: Métodos Estatísticos e Neuronais, Jorge Marques, IST-Press, A Brief Introduction to Machine Learning How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?
5 A Machine Learning System Experience Observations (External) Input/Query Learning System Output/Decision Models (Internal) Why Machine Learning? Huge Impact in Society, Economy, Health. Recent progresses in theory and algorithms. Computational power available. Growing amount of available data. Emerging Industries Study Human and Animal Learning
6 Problems Tackled by ML Techniques Data Mining: historical records to improve performance. Medical Records -> Medical Diagnosis Credit Records -> Credit Risk Analysis Systems that cannot be programmed by hand. Autonomous Driving Face Recognition Systems that have to adapt to the environment Newsreader that learns user interests Adaptive Control Systems Example: Datamining Given: 9714 patient records, each describing a pregnancy and birth. Each patient record contains 215 features. Learn to predict: Classes of future patients at high risk for Emergency Cesarean Section
7 Example: Datamining (cont.) One of the 18 learned rules -> Over training data: 26/41 =.63, Over test data: 12/20 =.60 If Then No previous normal delivery, and and Abnormal 2nd trimester ultrasound malpresentation at admission Probability of Emergency C-Section is 0.6 Example: Object Recognition Object Recognition is very hard to program problem. Learning approaches are more easily tackled:
8 Example: Robot Control Learning Learning to balance a pole. Must adapt the control law if the pole height changes. Example: Robot Cognitive Learning Robot learning from experience and human interaction.
9 Other Application Areas Human Computer Interfaces: Speech, Face Recognition Postal Automation: Handwritten digit recognition. Autonomous Driving: Learning road patterns. Bioinformatics: Learn models of gene expression. Disease Evolution: Learn disease dynamical models Medical Imaging: Detect anomalies in x-ray images, fmri, PET-scan, TAC, etc... What do we know? Excellent algorithms for pure induction SVM s, decision trees, graphical models, neural nets,... Algorithms for dimensionality reduction PCA, ICA, compression algorithms,... Fundamental information theoretic bounds relate data and biases to probability of successful learning PAC learning theory, statistical estimation, grammar induction,... Active learning by querying teacher is much more dataefficient than random observation Algorithms to learn from delayed feedback (reinforcement) Temporal difference learning, Q learning, policy iteration,...
10 What we do not know? Skill transfer How can skills learned in one domain be used in other? Co-training Can learning using multiple modalities simultaneously help the learning process? Never-ending learning Continuously updating the knowledge. Learning from instruction: lectures, discussion Role of emotions: motivation, forgetting, curiosity, fear, boredom,... Implicit (unconscious) versus explicit (deliberate) learning Human vs Machine Learning Can Human Learning Theories help deriving better Machine Learning Algorithms? Can Machine Learning Theory help understanding better Human Learning? Some analogies are being found between Machine and Human Learning: Reinforcement (TD) Learning <-> Dopamin system in the brain. Co-training <-> Intersensory Redundancy Hypothesis Dimensionality Reduction <-> Optimal sparse codes yield Gabor filters, as found in the visual cortex.
11 A Multidisciplinary Area Artificial Intelligence Optimization Philosophy Statistics Psychology and Neurobiology Control Theory Information Theory Computational Complexity Theory... Course Planning Fundamentals of Machine Learning Basis of Optimization Neural Networks The Generalization Problem Support Vector Machines Decision Trees Unsupervised Learning Probabilistic Methods Seminars
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