Please see this file.
Course | Instructor | Courseware | |
---|---|---|---|
March, 2019 | Multivariate Analysis | Hongxin Zhang | see below ... |
April, 2019 | Nonlinear Optimization | Hongxin Zhang | see below ... |
The course is open to Ph.D students of College of Computer Science and graduate students of related majors, Zhejiang University.
Tuesday, 18:30-21:30. Cao Guang Biao Building West 2-202, Yu Quan District, Zhejiang University.
Deadline: Please hand out your 1 course paper and 5 selected homework (or exercises) by 2019-06-01. 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:
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.
Topic | Date | Slides | Homework |
---|---|---|---|
Introduction | 2019.02.26 | Introduction | HW01 |
Why data driven | |||
Point estimation Additional reading | |||
Component Analysis | 2019.03.05 | PCA and its related techniques | HW02 |
Distance and similarity | 2019.03.12 | csmath-03-distance_and_similarity.pdf | HW03 |
Graphical Models | 2018.03.19 | Graphical Models Hidden Markov Models |
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.
Topic | Date | Slides | Homework |
---|---|---|---|
Linear programming | 2019.03.26 | Linear programming and simplex methods | |
Linear programming | 2019.04.02 | Dual methods | |
SVM | |||
Non-linear optimization | 2019.04.09 | NP | HW04 |
Quadratic programming | 2019.04.16 | QP | HW05 |
Further reading: https://github.com/ChristosChristofidis/awesome-deep-learning
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: