2010:ml
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| ===== Schedule ===== | ===== Schedule ===== | ||
| ^ Topic ^ Date ^ Slides ^ note ^ | ^ Topic ^ Date ^ Slides ^ note ^ | ||
| - | | Introduction | 2010.03.04 | {{2010:ml2010-introduction.pdf|Introduction}} | {{keynote:lesson01|=>}} | | + | | Introduction | 2010.03.04 | {{2010:ml2010-introduction.pdf|Introduction}} | [[keynote:lesson01|=>]] | |
| - | | ::: | ::: | {{2010:ml2010-what_is_ml.pdf|What is Machine Learning}} | | | + | | ::: | ::: | {{2010:ml2010-what_is_ml.pdf|What is Machine Learning}} | ::: | |
| - | | ::: | ::: | {{2010:ml2010-point_estimation.pdf|Point estimation}} | | | + | | ::: | ::: | {{2010:ml2010-point_estimation.pdf|Point estimation}} | ::: | |
| - | | Supervised Learning | 2010.03.11 | {{2010:ml2010-2-1-concept_learning.pdf|Concept learning}} | | | + | | Supervised Learning | 2010.03.11 | {{2010:ml2010-2-1-concept_learning.pdf|Concept learning}} | [[keynote:lesson02|=>]] | |
| - | | ::: | ::: | {{2010:ml2010-2-2-decision_tree.pdf|Decision tree}} | | | + | | ::: | ::: | {{2010:ml2010-2-2-decision_tree.pdf|Decision tree}} | ::: | |
| - | | ::: | ::: | {{2010:ml2010-2-3-naive_bayes_classification.pdf|Naive Bayes classifier}} | | | + | | ::: | ::: | {{2010:ml2010-2-3-naive_bayes_classification.pdf|Naive Bayes classifier}} | ::: | |
| - | | ::: | ::: | {{2010:ml2010-2-4-svm.pdf|Support Vector Machines}} | | | + | | ::: | ::: | {{2010:ml2010-2-4-svm.pdf|Support Vector Machines}} | ::: | |
| - | | ::: | ::: | {{2010:ml2010-2-5-boosting.pdf|Boosting}} | | | + | | ::: | ::: | {{2010:ml2010-2-5-boosting.pdf|Boosting}} | ::: | |
| - | | Unsupervised Learning | 2010.03.18 | --- | | | + | | Unsupervised Learning | 2010.03.18 | {{2010:ml2010-dimension_reduction.pdf| Dimension Reduction}} | [[keynote:lesson03|=>]] | |
| - | | Reinforcement Learning | 2010.03.25 | --- | | | + | | ::: | ::: | {{2010:adaboost.pdf| Why AddBoost?}} | ::: | |
| + | | ::: | ::: | {{2010:ml2010-clustering.pdf | Clustering }} | ::: | | ||
| + | | Reinforcement Learning | 2010.03.25 | {{2010:ml2010-clustering.pdf | Clustering (cont.)}} | [[keynote:lesson04|=>]] | | ||
| + | | ::: | ::: | {{2010:ml2010-hidden_markov_model.pdf|Hiden Markov Model}} | ::: | | ||
| + | | ::: | ::: | {{2010:ml2010-kalman_filter.pdf|Kalman Filter}} | ::: | | ||
| + | | course talk | 2010.04.01 | {{:2010:matrix-decomposition.pdf|Robust PCA}} | By Zhengfang Hu | | ||
| ===== Text books ===== | ===== Text books ===== | ||
| + | - [[http://www.cs.cmu.edu/~tom/mlbook.html|Machine learning]] | ||
| + | - [[http://research.microsoft.com/en-us/um/people/cmbishop/prml/|Pattern Recognition and Machine Learning ]] | ||
| + | - [[http://www.rii.ricoh.com/~stork/DHS.html|Pattern Classification (2nd ed) ]] | ||
| + | ===== Reference website ===== | ||
| + | - [[http://www.stanford.edu/class/cs229/|Stanford machine Learning course]] | ||
2010/ml.1268300231.txt.gz · Last modified: 2023/08/19 21:01 (external edit)
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