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  1. CH.0: Machine Leraning Instructor: Prof. Chen Sei-Wang (陳世旺), PhD Office : Applied Science Building, Room 101 Communication: E-mail:schen@csie.ntnu.edu.tw Tel : 7734-6661 Classroom: C003 Class Hr. : Mon. i) 2:20PM -- 3:30PM ii) 3:50PM -- 5:10PM Office Hr. : Mon. 10:00AM -- 12:00Noon Wed. 10:00AM -- 12:00Noon

  2. Teaching assistant : 謝日棠 Office : Room 108 Telephone : 7734-6668 E-mail : tommy31942@gmail.com Office Hrs. : Tue. 10:00AM -- 12:00Noon Wed. 10:00AM -- 12:00Noon 2

  3. Goal of Course Fundamental study on Machine Learning (ML), focusing on programming computers to optimize a performance criterion using example data or past experience. Within the field of data analytics, machine learning is an important method used to devise models and algorithms that lend themselves to prediction. This course will cover a wide range of research problems encountered within ML and provides detailed algorithmic and theoretical treatments for each.

  4. Theory oriented:boring Techniques oriented: too many techniques lead to hard to choose Foundation oriented: balance between theory and technique oriented approaches 4

  5. TextbookIntroduction to Machine Learning Third EditionEthem Alpaydin 2014(開發圖書,魏錦鈐, 0939852332, (02)82423988)

  6. Contents Ch. 1 IntroductionCh. 2 Supervised LearningCh. 3 Bayesian Decision TheoryCh. 4 Parametric MethodsCh. 5 Multivariate MethodsCh. 6 Dimensionality ReductionCh. 7 ClusteringCh. 8 Nonparametric MethodsCh. 9 Decision Trees Ch. 10 Linear Discrimination

  7. Ch. 11 Multilayer PerceptronsCh. 12 Local ModelsCh. 13 Kernel Machines Ch. 14 Graphical Models Ch. 15 Hidden Markov Models Ch. 16 Bayesian Estimation Ch. 17 Combining Multiple Learners Ch. 18 Reinforcement Learning Ch. 19 Design and Analysis of Machine Learning Experiments Appendix: Probability Theory

  8. Syllabus Ch.0: Syllabus, Ch.1: Introduction (第 1,2 週) Ch.2: Supervised Learning (第 2,3 週) Appendix: Probability Theory (第 3,4 週) Ch.3: Bayesian Decision Theory (第 5,6 週) Ch.4: Parametric Methods (第 6,7 週) Ch.5: Multivariate Methods (第 7,8 週) Mid-term Exam. (第 9 週) Ch.6: Dimensionality Reduction (第 10,11 週) Ch.7: Clustering (第 12 週) Ch.8: Nonparametric Methods (第 13 週) Ch.13: Kernel Machines (第 14,15 週) Ch.18: Reinforcement Learning (第 15,16 週) Presentation (第 17,18 週) 8

  9. Ch.9: Decision Trees (2 週) Ch.10: Linear Discrimination (2 週) Ch.14: Graphical Models (2 週) Ch.15: Hidden Markov Models (1 週) Ch.17: Combining Multiple Learners (2 週) Ch.19: Design and Analysis of Machine Learning Experiments (2週) 9

  10. Steps for finding the power points of chapters: • 資工系: http://www.csie.ntnu.edu.tw/csie/ • 系所成員: http://www.csie.ntnu.edu.tw/members/teacher.php • 名譽教授: http://www.csie.ntnu.edu.tw/members/teacher.php?class=120 • 網站: http://www2.csie.ntnu.edu.tw/~ipcv/ (5) Prof. Sei-Wang Chen (陳世旺教授) (6) Teaching (7) Machine Learning 10

  11. 1. Machine Learning by T. M. Mitchell, McGraw-Hill International Editions, 1997. 2. Learning from Data by Y. S. Abu-Mostafa, M. Magdon Ismail, and Hsuan-Tien Lin, AMLbook.com, 2012. 3. Understanding Machine Learning: From Theory to Algorithm by Shai Shalev-Shwartz and Shai Ben-David, 2014. 4. Fundamentals of Machine Learning for Predictive Data Analytics, Algorithm, Worked Examples, and Case Studies  by J. D. Kelleher, B. M. Namee, and A. D'Arcy, 2015. 5. Foundations of Machine Learning by M. Mohri, A. Rostamizadeh, and A. Talwalkar, 2018. References • Books 11

  12. Machine Learning by T. M. Mitchell, McGraw-Hill International Editions, 1997. Ch.1: Introduction, Ch.2: Concept Learning and General-to-Specific Ordering, Ch.3: Decision Tree Learning, Ch.4: Artificial Neural Networks, Ch.5: Evaluating Hypotheses, Ch.6: Bayesian Learning, Ch.7: Computational Learning Theory, Ch.8: Instance-Based Learning, Ch.9: Generic Algorithms, Ch.10: Learning Sets of Rules, Ch.11: Analytical Learning, Ch.12: Combining Inductive and Analytical Learning, Ch.13: Reinforcement Linear 12

  13. 2. Learning from Data by Y. S. Abu-Mostafa, M. Magdon-Ismail, and Hsuan-Tien Lin, AMLbook.com, 2012. Ch.1: The Learning Problems, Ch.2: Training versus Testing, Ch.3: The Linear Model, Ch.4: Overfitting, Ch.5: Three Learning Principles, Appendix: Proof of the VC Bound 3. Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben- David, 2014. Ch.1: Introduction, Ch.2: A Gentle Start, Ch.3: A Formal Learning Model, Ch.4: Learning via Uniform Convergence, 13

  14. Ch.5: The Bias-Complexity Trade-off, Ch.6: The VC-Dimension, Ch.7: Non-uniform Learnability, Ch.8: The Runtime of Learning, Ch.9: Linear Predictors, Ch.10: Boosting, Ch.11: Model Selection and Validation, Ch.12: Convex Learning Problems, Ch.13: Regularization and Stability, Ch.14: Stochastic Gradient Descent, Ch.15: Support Vector Machine, Ch.16: Kernel Methods, Ch.17: Multiclass, Ranking, and Complex Prediction Problems, Ch.18: Decision Trees, Ch.19: Nearest Neighbor, Ch.20: Neural Networks, Ch.21: Online Learning, Ch.22: Clustering, Ch.23: Dimensionality Reduction, Ch.24: Generative Models, Ch.25: Feature Selection and Generation, Ch.26: Rademacher Complexity, 14

  15. Ch.27: Covering Numbers, Ch.28: Proof of the Fundamental Theorem of Learning Theory, Ch.29: Multiclass Learnability, Ch.30: Compression Bounds, Ch.31: PAC-Bayes, Appendix A: Technical Lemmas, Appendix B: Measure Concentration, Appendix C: Linear Algebra 4. Fundamentals of Machine Learning for Predictive Data Analytics, Algorithm, Worked Examples, and Case Studies by J. D. Kelleher, B. M. Namee, and A. D'Arcy, 2015. Ch.1: Machine Learning for Predictive Data Analytics, Ch.2: Data to Insights to Decisions, Ch.3: Data Exploration, Ch.4: Information-based Learning, Ch.5: Similarity-based Learning, 15

  16. Ch.6: Probability-based Learning, Ch.7: Error-based Learning, Ch.8: Evaluation, Ch.9: Case Study: Customer Churn, Ch.10: Case Study: Galaxy Classification, Ch.11: The Art of Machine Learning for Predictive Data Analytics Appendix A: Descriptive Statistics and Data Visualization for Machine Learning, Appendix B: Introduction to Probability for Machine Learning, Appendix C: Differentiation Techniques for Machine Learning 5. Foundations of Machine Learning by M. Mohri, A. Rostamizadeh, and A. Talwalkar, 2018. Ch.1: Introduction, Ch.2: The PAC Learning Framework, Ch.3: Rademacher Complexity and VC-Dimension, Ch.4: Model Selection, Ch.5: SVM, Ch.6: Kernel Machines, 16

  17. Ch.7: Boosting, Ch.8: On-Line Learning, Ch.9: Multi-class Classification, Ch.10: Ranking, Ch.11: Regression, Ch.12: Maximum Entropy Models, Ch.13: Conditional Maximum Entropy Models, Ch.14: Algorithmic Stability, Ch.15: Dimensionality Reduction, Ch.16: Learning Automata and Languages, Ch.17: Reinforcement Learning Appendix A: Linear Algebra Review, Appendix B: Convex Optimization, Appendix C: Probability Review, Appendix D: Concentration Inequalities, Appendix E: Notions of Information Theory 17

  18. Journels: • IEEE Trans. on Machine Learning • IEEE Transactions on Neural Networks • IEEE Transactions on Pattern Analysis and Machine Intelligence • Journal of Machine Learning Research • Neural Computation • Neural Networks 18

  19. (C) Procedings: • International Conference on Machine Learning • International Conference on Machine Learning and Computing • European Conference on Machine Learning • Neural Information Processing Systems • Uncertainty in Artificial Intelligence • Computational Learning Theory • International Joint Conference on Artificial Intelligence • International Conference on Neural Networks • Advanced Lectures on Machine Learn • Pattern Recognition and Machine Learning 19

  20. (D) Web Package Scikit-Learn for Machine Learning: http://scikit-learn.org/stable/user_guide.html https://machine-learning-python.kspax.io/Introduction/intro.html http://scikit-learn.org/stable/ 20 20

  21. Assignment Submission (A) Prepare the assignment file Content: (1) Problem statement (2) (i) General assignment – Answer (ii) Project assignment – (a) Input/Output data (b) Source code (c) Comments File Name: Name.hw# , e.g., 陳世旺.HW1

  22. (B) Submit the file to: (1) Moodle https://moodle.ntnu.edu.tw/ (2)登入身份

  23. (3) 點選課程 (4) 點選所要交之作業區 e.g., Homework1 (5) 上傳作業 作業繳交時間在下一次上課前15分鐘

  24. Evaluation Interaction 20% Homework 30% Examination 25% Presentation 20% Late homeworks will not be accepted Plagiarism is definitely not allowed 24