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2018 [2018/04/17 17:52]
hongxin [Schedule]
2018 [2021/04/13 21:35] (current)
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 +====== CSMATH ​ (2017-2018) ======
 +
 +===== About =====
 +Please see {{:​2017:​csmath-01-introduction.pdf |this file}}.
 +
 +|              ^  Course ​                       ^  Instructor ​                                                ​^ ​ Courseware ​        ​^  ​
 +^ March, 2018  |  Multivariate Analysis ​        | [[ http://​www.cad.zju.edu.cn/​home/​zhx/​ | Hongxin Zhang ]]   ​| ​ [[2017&#​courselet_on_multivariate_analysis|see below ...]]   ​| ​  
 +^ April, 2018   ​| ​ Nonlinear Optimization ​       | [[ http://​www.cad.zju.edu.cn/​home/​zhx/​ | Hongxin Zhang ]]   ​| ​ [[2017#​courselet_on_optimization|see below ...]]  |  ​
 +
 +===== 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 =====
 +Tuesday, 18:​30-21:​30. Cao Guang Biao Building West 2-202, Yu Quan District, Zhejiang University.
 +
 +===== Homeworks =====
 +
 +**Deadline**:​ Please hand out your **1** [[cp:​2018|course paper]] and **5** selected homework (or exercises) by 2017-06-01. All homework and exercises must be implemented in Python and with TensorFlow (optionally)
 +
 +  * [[homework:​2017-py01|Homework 01]] => Curve fitting ​
 +  * [[homework:​2017-py02|Homework 02]] => PCA 
 +  * [[homework:​2017-py03|Homework 03]] => 2D MOG and k-means
 +  * [[homework:​2017-py04|Homework 04]] => L-M algorithm
 +  * [[homework:​2017-py05|Homework 05]] => 2D SVM
 +and the report of homework please follow {{ :​2018:​jupyternotebook.pdf | this instruction}}.
 +
 +More optional exercises:
 +  * [[homework:​2017-ex01|Excercise 01]] => Gaussian distribution and its conjugate prior
 +  * [[homework:​2017-ex02|Excercise 02]] => kernel PCA
 +  * [[homework:​2017-ex03|Excercise 03]] => RPCA 
 +  * [[homework:​2017-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 ​          | 2018.03.06 | {{:​2017:​csmath-01-introduction.pdf|Introduction}} ​           |  [[homework:​2017-py01|HW01]] ​        ​|  ​
 +| :::                    | :::        | {{:​2017:​csmath-01-data-driven.pdf|Why data driven}} ​         |  :::                            |
 +| :::                    | :::        | {{:​2017:​csmath-01-point_estimation.pdf|Point estimation}} ​ [[https://​engineering.purdue.edu/​kak/​Trinity.pdf|Additional reading]] ​ |  :::                            |
 +| Component Analysis ​    | 2018.03.13 | {{:​2017:​csmath-02-component_analysis.pdf|PCA and its related techniques}}| ​ [[homework:​2013-py02|HW02]] ​        |
 +| Distance and similarity | 2018.03.20 | {{:​2017:​csmath-03-distance_and_similarity.pdf|}} |  [[homework:​2013-py03|HW03]] ​         |
 +| Graphical Models ​      | 2018.03.27 | {{:​2015:​csmath-04-graphical_models_a.pdf|Graphical Models}} {{:​2015:​csmath-04-graphical_models_b.pdf|Hidden Markov 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 ​      | 2018.04.03 |  {{:​2018:​csmath-05-linear_programming.pdf|Linear programming and simplex methods}} ​      ​| ​                              ​| ​
 +| Linear programming ​      | 2018.04.10 |  {{:​2018:​csmath-06-linear_programming_and_dual_methods.pdf|Dual methods}} ​      ​| ​                              |
 +|                          |            |  {{:​svm_cjlin_dm.pdf|SVM}} ​       |                               |
 +| Non-linear optimization ​ | 2018.04.17 |  {{:​2018:​csmath-07-nonlinear.pdf|NP}} ​      ​| ​ [[homework:​2017-py04|HW04]] ​                        |
 +| Quadratic programming ​   | 2018.04.24 |  {{:​2017:​csmath-08-nonlinear_and_qp.pdf|QP}} ​       |  [[homework:​2017-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. ​
 +
 +Further reading: https://​github.com/​ChristosChristofidis/​awesome-deep-learning
 +
 +
 +
 +====== 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:
 +  * Plot the results in [[http://​matplotlib.org/​|matplotlib]] and [[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]]
 +  ​
 +  ​
 +  ​
 +  ​
  
2018.txt · Last modified: 2021/04/13 21:35 (external edit)