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Machine learning
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- 2. Introduction Machine Learning isa subset of Artificial Intelligence that, according to Arthur Samuel in 1959, gives "computers the ability to learn without being explicitly programmed.” It evolved from the study of pattern recognition and computational learning theory in artificial intelligence “Machine learning refers to a system capable of the autonomous acquisition and integration of knowledge.”
- 3. What is MachineLearning? Tom Mitchell (1998) Well-posed Learning Problem: A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. Learning = Improving with experience at some task Improve over task T, With respect to performance measure, P Based on experience, E.
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- 5. Why Machine Learning? No human experts industrial/manufacturing control mass spectrometer analysis, drug design, astronomic discovery Black-box human expertise face/handwriting/speech recognition driving a car, flying a plane Rapidly changing phenomena credit scoring, financial modeling diagnosis, fraud detection Need for customization/personalization personalized news reader movie/book recommendation
- 6. 6 What We TalkAbout When We Talk About“Learning” 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 “Da Vinci Code” also bought “The Five People You Meet in Heaven” (www.amazon.com) Build a model that is a good and useful approximation to the data.
- 7. ML in aNutshell Tens of thousands of machine learning algorithms Hundreds new every year Every machine learning algorithm has three components: Representation Evaluation Optimization
- 8. Representation Decision trees Sets of rules / Logic programs Instances Graphical models (Bayes/Markov nets) Neural networks Support vector machines Model ensembles Etc.
- 9. Evaluation Accuracy Precisionand recall Squared error Likelihood Posterior probability Cost / Utility Margin Entropy K-L divergence Etc.
- 10. Optimization Combinatorial optimization E.g.: Greedy search Convex optimization E.g.: Gradient descent Constrained optimization E.g.: Linear programming
- 11. Types of Learning Supervised (inductive) learning Training data includes desired outputs Unsupervised learning Training data does not include desired outputs Semi-supervised learning Training data includes a few desired outputs Reinforcement learning Rewards from sequence of actions
- 12. Supervised learning( ) Prediction Classification (discrete labels), Regression (real values) Decision tree induction Rule induction Instance-based learning Bayesian learning Neural networks Support vector machines Model ensembles Learning theory Algorithms
- 13. Supervised Learning:Uses Example: decision trees tools that create rules 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 Algorithms
- 14. Unsupervised learning( ) Clustering Probability distribution estimation Finding association (in features) Dimension reduction Semi-supervised learning Reinforcement learning Decision making (robot, chess machine) Recommender Systems Algorithms
- 15. Web Search Finance E-commerce Robotics Space Exploration Information Extraction Social Networks Face detection and recognition Object detection and recognition Image segmentation Multimedia event detection Economical and commercial usage Speech recognition Healthcare Weather forecast Applications
- 16. Related Fields Machine learningis primarily concerned with the accuracy and effectiveness of the computer system. psychological models data mining cognitive science decision theory information theory databases machine learning neuroscience statistics evolutionary models control theory
- 17. We have asimple overview of some techniques and algorithms in machine learning. Furthermore, there are more and more techniques apply machine learning as a solution. In the future, machine learning will play an important role in our daily life. Conclusion
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