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2011:mva [2011/02/24 17:16]
hongxin [Schedule]
2011:mva [2023/08/19 21:02] (current)
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 +====== Multivariate Analysis ======
 +The study of learning from data is commercially and scientifically important. This one month short course is designed to give first year Ph.D. students a thorough grounding in the methodologies,​ technologies,​ mathematics and algorithms currently needed by people who do research in learning and data mining or who may need to apply learning or data mining techniques to a target problem. The topics of the course draw from classical statistics, from machine learning, from data mining, from Bayesian statistics and from statistical algorithmics. \\ 
 +Students entering the class should have a pre-existing working knowledge of probability,​ statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. ​
  
 +===== Schedule =====
 +^    Topic               ​^ ​  ​Date ​    ​^ ​ Slides ​                                                   ^   ​note ​   ^
 +| Introduction ​          | 2011.02.24 | {{:​2011:​mva2011-introduction.pdf|Introduction}} ​           |  [[keynote:​2011-lesson01|=>​]] ​        ​|  ​
 +| :::                    | :::        | {{:​2011:​mva2011-data-driven.pdf|Why data driven}} ​         |  :::                            |
 +| :::                    | :::        | {{:​2011:​mva2011-point_estimation.pdf|Point estimation}} ​   |  :::                            |
 +| Component Analysis ​    | 2011.03.03 | {{:​2011:​mva2011-component_analysis.pdf|PCA and its related techniques}}| ​ [[keynote:​2011-lesson02|=>​]] ​        |
 +| Distance and similarity | 2011.03.10 | {{:​2011:​mva2011-distance_and_similarity.pdf|Distance,​ similarity and clustering}} |  [[keynote:​2011-lesson03|=>​]] ​        |
 +| Graphical Models ​      | 2011.03.17 | {{:​2011:​mva2011-graphical_models.pdf|Graphical Models}} ​                                                          ​| ​  ​[[keynote:​2011-lesson04| ​  ​=>​]] ​        |
 +| Course talk (Ibrar Hussain) ​ | 2011.03.17 | {{:​2011:​presentation_ml_by_ibrar.pdf|Clustering in Machine Learning}} ​  ​| ​ ---  |
 +
 +
 +===== Text books =====
 +  - [[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)  ]]
 +  - [[http://​www-stat.stanford.edu/​~tibs/​ElemStatLearn/​|The Elements of Statistical Learning: Data Mining, Inference, and Prediction. ​ Second Edition, 2009.]]
 +
 +
 +
 +===== Reference website =====
 +  - [[http://​www.stanford.edu/​class/​cs229/​|Stanford machine Learning course]]