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2018

CSMATH (2017-2018)

About

Please see this file.

Course Instructor Courseware
March, 2018 Multivariate Analysis Hongxin Zhang see below ...
April, 2018 Nonlinear Optimization Hongxin Zhang 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 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)

and the report of homework please follow this instruction.

More optional exercises:

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 Introduction HW01
Why data driven
Point estimation Additional reading
Component Analysis 2018.03.13 PCA and its related techniques HW02
Distance and similarity 2018.03.20 csmath-03-distance_and_similarity.pdf HW03
Graphical Models 2018.03.27 Graphical Models Hidden Markov Models

Text books

Reference website

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 Linear programming and simplex methods
Linear programming 2018.04.10 Dual methods
SVM
Non-linear optimization 2018.04.17 NP HW04
Quadratic programming 2018.04.24 QP HW05

Text books

  1. 袁亚湘,孙文瑜. 最优化理论与方法,科学出版社.
  2. 张建中,许绍吉. 线性规划. 科学出版社.
  3. 黄红选,韩继业. 数学规划. 清华大学出版社.
  4. Stephen Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press.

Further reading: https://github.com/ChristosChristofidis/awesome-deep-learning

Python

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:

Most useful Python packages in this course:

Other related modules:

2018.txt · Last modified: 2018/04/17 09:52 by hongxin