1. Introduction to Machine Learning August, 2014 Vũ Việt Vũ Computer Engineering Division, Electronics Faculty Thai Nguyen University of Technology

2. Outline What is Machine Learning? Application of Machine Learning Research group in Machine Learning Conclusion

3. What is the Machine Learning? Machine learning is a subfield of computer science and artificial intelligence that deals with the construction and study of systems that can learn from data.computer scienceartificial intelligencelearn

4. What is the Machine Learning? AI = Artificial Intelligence

5. Supervised learning problem Unsupervised learning problem Semi-supervised learning problem Active learning prblem Problems of Machine learning

6. Supervised learning Given a training data set (labeled data) T = {x 1, x 2,…,x n }; x i : vector with m dimensions and x i belongs to a class C v, v = 1…m. Task: Build a classifier model to predict the label of a new data x new. T x new

7. Example Face recognition ? T: Traning data x new

8. Example (cont.) Recognition Result Text Picture What is the T here? What is the x new here? CAR NUMBER RECOGNITION

9. Methods -Support Vector machine -Neural Network -Decision Trees, -K-nearest neighbor graph,…

10. Unsupervised learning Unsupervised learning: The objective of unsupervised learning is to discover structures in the data. Example: clustering, outlier detection,... How many clusters here?

11. Example

12. Methods for clustering K-means clustering Density-based clustering Fuzzy-Cmeans clustering Hierarchical clustering, Graph-based clustering

13. Semi-supervised learning Semi-supervised learning combines both labeled and unlabeled examples to build the classifier model or to discover structures in data. Methods: - Self trainning, - Support Vector Machine, - Graph-based methods

14. Active learning Input Data Active Learning Questions Users (Experts) Response Machine learning algorithm Labeled data Output 14 [ Vu et al, ECAI2010 Vu et al, ICPR2010 Vu et al, Pattern Recogntion’12] Active learning is a special case of semi-supervised learning in which a learning algorithm is able to interactively query the user to obtain the outputs at new data points.semi-supervised learning

15. Application of Machine learning Computer vision Object Recognition Robotics (ASIMO,...) Natural language processing Search engines (Google, Yahoo) Medical diagnosis Bioinformatics Stock market analysis Classifying DNA sequences Speed and handwriting recognition Game playing Software engineering Adaptive website Computational finance Recommender systems

16. Research Group of Machine Learning Dr. Vu Viet Vu, Thai Nguyen University of Technology Prof. Nicolas Labroche, France Prof. Violaine Antoine, France Prof. Le Ba Dung: Institute of Information Technology, Viet Nam Dr. Vu Hai: Ha Noi University of Technology Dr. Nguyen Thi Oanh: Ha Noi University of Technology PhD student. Nguyen Manh Tuan: Institute of Information Technology, Viet Nam Theory: Unsupervised learning, clustering, active learning,... Application: Image processing, object recognition

17. Publication Vu Viet Vu, Nicolas Labroche, and Violaine Antoine. Semi-supervised graphe-based clustering. Submitted to Pattern Recognition Journal (ISI), 2014. Violaine Antoine, Nicolas Labroche, Vu Viet Vu. Evidential seed-based semi- supervised clustering. Submitted to the 7th International Conference on Soft Computing and Intelligent Systems and 15th International Symposium on Advanced Intelligent Systems, Japan, 2014 Vu Viet Vu, Nicolas Labroche, Violaine Antoine, and Le Ba Dung, Active seeds selection with a k-nearest neighbors graph, In proceeding of the first NAFOSTED Conference on Information and Computer Science (NICS'14), Ha Noi, Viet Nam, pp: 386-395. Selected to publish in Advances in Intelligent Systems and Computing, Springer. 2014.

18. Publication (cont.) Vu Viet Vu, Nicolas Labroche, and Bernadette Bouchon-Meunier, Viet-Thang Vu, and Nguyen Thi Thu Hien. Graph based Semi-supervised Clustering. Journal of Science and Technology, Ha Noi University of Education, Viet Nam, March- 2013. Vu Viet Vu, Nicolas Labroche, and Bernadette Bouchon-Meunier. Improving Constrained Clustering with Active Query Selection. Pattern Recognition 45(4): 1749-1758 [SCI], ISSN: 0031-3203, 2012Pattern Recognition 45 Vu Viet Vu, Nicolas Labroche, and Bernadette Bouchon-Meunier. An Efficient Active Constraint Selection Algorithm for Clustering. In Proc. of the 20th IEEE International Conference on Pattern Recognition (ICPR-2010), Istanbul, Turkey, August, 2010ICPR-2010 Vu Viet Vu, Nicolas Labroche, and Bernadette Bouchon-Meunier. Boosting Clustering by Active Constraint Selection. In Proc. of the 19th European Conference on Artificial Intelligence (ECAI-2010), Lisbon, Portugal, August, 2010ECAI-2010 Vu Viet Vu, Nicolas Labroche, and Bernadette Bouchon-Meunier. Active Learning for Semi- Supervised K-Means Clustering. In Proc. of the 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2010), Arras, France.10.2010 ICTAI-2010 Vu Viet Vu, Nicolas Labroche, and Bernadette Bouchon-Meunier. Leader Ant Clustering with Constraints. In Proc. of the 7th IEEE International Conference on Computing and Communication Technologies (IEEE-RIVF-2009), Danang, Vietnam, July, 2009

19. Conclusion Developing new methods for machine learning Using machine learning methods for real applications: image processing, pattern recognition, speed processing,... The courses at TNUT: Artificial Intelligence, Image Processing, Speed Processing, Algorithm theory,...

20. Thank you for your attention!