Data Driven Modeling 课程

1.Core Course

MSDM 5001 Introduction to Computational and Modeling Tools

The basics about CPU, GPU and their applications in high performance computing; introduction of the operating systems; introduction of the parallel program design, implementation and applications in physics and other areas; basics about quantum computation: the concept, algorithm and future hardware.

数值计算的工具，并行程序设计，量子计算

MSDM 5002 Scientific Programming and Visualization

The Python programming language and its application to scientific programming (packages such as Scipy, Numpy, Matplotlib); introduction to Matlab, Mathematica, Excel and R; visualization techniques for data from scientific computing, everyday life, social media, business, medical imaging, etc. (stock price, housing price, highway traffic data, weather data, fluid dynamics data) (3 hours lecture in computer lab)

程序软件以及可视化的实现

MSDM 5003 Stochastic Processes and Applications

Probability theory; maximum likelihood; Bayesian techniques; principal component analysis, data transformation and filtering; Brownian motion and stochastic processes; cross-correlations; power laws; log-normal distribution and extreme value distributions; Maxwell-Boltzmann distribution; Monte Carlo methods; agent-based models; evolutionary games; Black-Scholes equation.

随机过程，概率论，极大似然，贝叶斯，主成分分析，数据处理，布朗运动……

MSDM 5004 Numerical Methods and Modeling in Science

Fundamental numerical techniques: error, speed and stability, integrals, derivatives, interpolation and extrapolation, least squares fitting, solution of linear algebraic equations, mathematical optimization, ordinary differential equations, partial differential equations; Fourier and spectral applications, random processes, Monte Carlo simulations, simulated annealing.

数值方法，误差，速度，稳定性……解决线性代数方程，偏微分方程，傅里叶以及光谱应用过……

MSDM 5005 Innovation in Practice

Three topics will be selected each term. For each topic, specialists from the industry will be invited to introduce the industrial landscape and related issues. Students will then form groups to explore methodology of collecting useful data and propose innovative solutions related to the topics based on real data. This course enables students to apply mathematical theories to real context and gives students hands-on experience on data science.

实践应用

2. Elective Courses

MSDM 5051 Algorithm and Object-Oriented Programming for Modeling

Data structures (such as list, queue, stack), algorithms (such as recursion, sorting and searching), concepts and design patterns of object-oriented programming are introduced. Students are expected to understand and use these techniques to handle data.

数据结构，递归排序以及搜索，面向程序编程

MSDM 5053 Quantitative Analysis of Time Series

The course introduces some fundamental concepts of time series, including strict stationarity and weak stationarity, and series correlation. Students will study some classical time series models, including autoregressive model, moving averages model and ARMA model, seasonal ARIMA models, multivariate time series models, and some new financial time series models, including ARCH and GARCH models. Students will also learn the forecasting techniques based on those time series models and build up time series models for real time series data in natural science, engineering and economics.

计量经济，时间序列

MSDM 5054 Statistical Machine Learning

This course introduces modern methodologies in machine learning, including tools in both supervised learning and unsupervised learning. Examples include linear regression and classification, tree-based methods, kernel methods and principal component analysis. Students will practice R or Python, and apply them to real data analysis.

机器学习R语言或Python

MSDM 5056 Network Modeling

Empirical study of networks in social science, economics, finance, biology and technology, network models: random networks, small world networks, scale free networks, spatial and hierarchical networks, evolving networks, methods to generate them with a computer, dynamical processes on complex networks: network search, epidemic spreading, rumor and information spreading, community detection algorithms, applications of network theory.

网络建模，图论

MSDM 5058 Information Science

This course will cover: (1) decision theory and its applications to finance; options and payoff diagrams, binomial trees; (2) portfolio management of financial time series using mean variance analysis; (3) evolutionary computation for optimization, with applications in finding good prediction rules in finance; (4) measure of information, various information entropies, and methods of maximum entropy; (5) game theory and its applications in competitive situations; (6) multi-agent systems modelling and applications to social networks and financial systems.

信息科学，帮助决策，二叉树模型，平均方差分析，熵，最大熵模型，博弈论

MSDM 5059 Numerical Optimization and Applications

This course will introduce the concepts and techniques of optimization and modeling in systems and applications with many variables and constraints. Topics to be discussed include Linear programming, network flow models, project management, convex sets, duality, Lagrange multipliers, 1-D optimization algorithms, unconstrained optimization, guided random search methods, and constrained optimization.

数值优化，凸问题，有无限制……

MSDM 6980 Computational Modeling and Simulation Project

Under the supervision of a faculty member, students will carry out an independent research project on computational modeling and simulation. At the end of the course, students need to summarize their results in the form of short theses and give oral presentations. Enrollment in the course requires approval by the course coordinator and supervisor.

独立项目，论文和口头演示

PHYS 5120 Computational Energy Materials and Electronic Structure Simulations

This course provides students with training in performing numerical simulations in physics problems. Topics include linear algebra, ordinary and partial differential equations, and stochastic processes. Numerical techniques learned in these areas will be used to solve various problems in classical, atomic, condensed matter, statistical, and bio-physics.
Background: UG level physics, working knowledge of the programming language C or C++
Exclusion: MSDM 5004 Numerical Methods and Modeling in Science (effective from 1 August 2018)