Máy học tự động - Machine learning

1. Giới thiệu (slides.vn) - Introduction (slides.en)
2. k láng giềng (slides.vn) - k nearest neighbors (slides.en)
3. Đánh giá mô hình (slides.vn) - Evaluation (slides.en)
4. Bayes thơ ngây (slides.vn) - Naive Bayes (slides.en)
5. Cây quyết định (slides.vn) - Decision trees (slides.en)
6. Tập hợp mô hình (slides.vn) - Ensemble-based learning (slides.en)
7. Mạng nơ-ron (slides.vn) - Neural networks (slides.en)
8. Máy học SVM (slides.vn) - Support vector machines (slides.en)
9. Gom cụm dữ liệu (slides.vn) - Clustering (slides.en)

T. Hastie, R. Tibshirani, J. Friedman (2013)
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Springer, 2 edition

C. Bishop (2006)
Pattern Recognition and Machine Learning
Springer-Verlag New York

R. Quinlan (1993)
C4.5: Programs for Machine Learning
Morgan Kaufmann Publishers

R. Schapire and Y. Freund (2012)
Boosting: Foundations and Algorithms
MIT Press

L. Breiman (1996)
Bagging predictors
Machine Learning Vol. 24(2):123–140

L. Breiman (2001)
Random forests
Machine Learning Vol. 45(1):5–32

S. Haykin (1998)
Neural Networks: A Comprehensive Foundation
Prentice Hall

N. Cristianini and J. Shawe-Taylor (2000)
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cambridge University Press

V. Vapnik (1995)
The Nature of Statistical Learning Theory
Springer-Verlag, New York

T. Mitchell (1997)
Machine Learning
McGraw Hill