2014

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 2014 [2014/05/22 16:34] 2014 [2021/04/13 21:35] (current) 2014/04/17 23:46 hongxin [Schedule] 2014/04/17 23:45 hongxin [Text books] 2014/04/17 23:32 hongxin [Schedule] 2014/03/29 14:34 hongxin [Schedule] 2014/03/14 11:15 hongxin [Schedule] 2014/03/06 18:17 hongxin [Schedule] 2014/02/20 13:42 hongxin [Homeworks] 2014/02/20 13:41 hongxin [Schedule] 2014/02/20 13:40 hongxin [Schedule] 2014/02/20 13:40 hongxin [About] 2014/02/20 13:27 hongxin [CSMATH (2012-2013)] 2014/02/20 13:27 hongxin created Line 1: Line 1: + ====== CSMATH ​ (2013-2014) ====== + ===== About ===== + Please see {{:​2013:​csmath-01-introduction.pdf|this file}} and [[keynote:​2013-lesson00|this link]]. + + + |              ^  Course ​                       ^  Instructor ​                                                ​^ ​ Courseware ​        ​^ ​ Keynote ​ ^ + ^ March, 2014  |  Multivariate Analysis ​        | [[ http://​www.cad.zju.edu.cn/​home/​zhx/​ | Hongxin Zhang ]]   ​| ​ [[2013&#​courselet_on_multivariate_analysis|see below ...]]   ​| ​  ​[[keynote:​2013-lesson00|0]] [[keynote:​2013-lesson01|1]] [[keynote:​2013-lesson02|2]] [[keynote:​2013-lesson03|3]] [[keynote:​2013-lesson04|4]] ​ | + ^ April, 2014   ​| ​ Nonlinear Optimization ​       | [[ http://​www.cad.zju.edu.cn/​home/​zhx/​ | Hongxin Zhang ]]   ​| ​ [[2013#​courselet_on_optimization|see below ...]]  |  [[keynote:​2013-lesson05|5]] [[keynote:​2013-lesson06|6]] [[keynote:​2013-lesson07|7]] [[keynote:​2013-lesson08|8]] ​ | + + ===== Students ===== + The course is open to Ph.D students of College of Computer Science and graduate students of related majors, Zhejiang University. ​ + + ===== Time and Place ===== + Thursday, 18:​30-21:​30. Cao Guang Biao Building West 2-202, Yu Quan District, Zhejiang University. + + ===== Homeworks ===== + + **Deadline**:​ Please hand out your **1** [[cp:​2013|course paper]] and **5** selected homework (or exercises) by 2014-06-01. + + * [[homework:​2013-py01|Homework 01]] => curve fitting ​ + * [[homework:​2013-py02|Homework 02]] => PCA + * [[homework:​2013-py03|Homework 03]] => 2D MOG and k-means + * [[homework:​2013-py04|Homework 04]] => L-M algorithm + * [[homework:​2013-py05|Homework 05]] => 2D SVM + + More optional excercises: + * [[homework:​2013-ex01|Excercise 01]] => Gaussian distribution and its conjugate prior + * [[homework:​2013-ex02|Excercise 02]] => kernel PCA + * [[homework:​2013-ex03|Excercise 03]] => RPCA + * [[homework:​2013-ex04|Excercise 04]] => ISOMAP and LLE + * ... + + + + ====== Courselet on 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 ​                                                   ^   ​Homework ​ ^ + | Introduction ​          | 2014.02.27 | {{:​2014:​csmath-01-introduction.pdf|Introduction}} ​           |  [[homework:​2013-py01|HW01]] ​        ​|  ​ + | :::                    | :::        | {{:​2014:​csmath-01-data-driven.pdf|why data driven}} ​         |  :::                            | + | :::                    | :::        | {{:​2014:​csmath-01-point_estimation.pdf|point estimation}} ​  ​| ​ :::                            | + | Component Analysis ​    | 2014.03.06 | {{:​2014:​csmath-02-component_analysis.pdf|PCA and its related techniques}}| ​ [[homework:​2013-py02|HW02]] ​        | + | Distance and similarity | 2014.03.13 | {{:​2014:​csmath-03-distance_and_similarity.pdf|distance,​ similarity and clustering}} |  [[homework:​2013-py03|HW03]] ​         | + | Graphical Models ​      | 2014.03.20 | {{:​2013:​csmath-04-graphical_models.pdf|graphical models}} ​                                                          ​| ​           | + + ===== 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]] + + + ====== Courselet on Optimization ====== + Optimization methods, both linear and non-linear ones, are important mathematical techniques for computer science. 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 are doing research related to linear and non-linear optimization. The topics of the course draw mainly from linear programming,​ quadratic programming and nonlinear optimization.\\ + + Students entering the class should have a pre-existing working knowledge of fundamental mathematics 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 ​                          ​^ ​        ​Homework ​             ^ + | Linear programming ​      | 2014.03.27 |   ​{{:​2014:​csmath-05-linear_programming.pdf|LP}} ​      ​| ​                              ​| ​ + | Linear programming ​      | 2014.04.03 |   ​{{:​2014:​csmath-06-linear_programming_and_dual_methods.pdf|LP}} ​      ​| ​                              | + |                          |            |   ​{{:​svm_cjlin_dm.pdf|SVM}} ​       |                               | + | Non-linear optimization ​ | 2014.04.10 |   ​{{:​2014:​csmath-07-nonlinear.pdf|NP}} ​      ​| ​ [[homework:​2013-py04|HW04]] ​                        | + | Quadratic programming ​   | 2014.04.17 |   ​{{:​2014:​csmath-08-nonlinear_and_qp.pdf|QP}} ​      ​| ​ [[homework:​2013-py05|HW05]] ​                        | + + ===== Text books ===== + - 袁亚湘，孙文瑜. 最优化理论与方法，科学出版社. + - 张建中，许绍吉. 线性规划. 科学出版社. ​ + - 黄红选，韩继业. 数学规划. 清华大学出版社. + - Stephen Boyd and Lieven Vandenberghe. [[https://​www.stanford.edu/​~boyd/​cvxbook/​|Convex Optimization]]. Cambridge University Press. ​ + + + + ====== Python ====== + + [[http://​www.python.org/​|Python]] is a powerful but easy-to-use script language for daily software development. In this course, we mainly use it as a standard training tool to temper mathematical skills. Several useful links are listed as following for your reference. + + Learning Python: + * An active Chinese python forum: [[http://​wiki.woodpecker.org.cn/​moin/​|http://​wiki.woodpecker.org.cn]]. You can find a  [[http://​wiki.woodpecker.org.cn/​moin/​March_Liu/​PyTutorial|Chinese tutorial book]] in this website. Alternatively,​ you can read the following simplified course in Chinese [[http://​woodpecker.org.cn/​abyteofpython_cn/​chinese/​]]. + * [[http://​www.sthurlow.com/​python/​|A Beginner'​s Python Tutorial]] in English + * [[http://​docs.python.org/​tutorial/​|The Python (V2.7) Tutorial]] ​ + * [[http://​www.mindview.net/​Books/​TIPython|Think in python]] + + Most useful Python packages in this course: + * [[http://​gnuplot-py.sourceforge.net/​|gnuplot]] + * [[http://​numpy.scipy.org/​|NumPy]] is the fundamental package needed for scientific computing with Python. + * [[http://​conference.scipy.org/​scipy2010/​|SciPy]] + + Other related modules: + * [[http://​folk.uio.no/​henninri/​pca_module/​|PCA Module for Python]] + * [[http://​www-users.cs.york.ac.uk/​jc/​teaching/​agm/​|Algorithms for Graphical Models]] + ​ + ​ + ​