STAT Courses
Undergraduate Course List
Course Categories
- Courses For the Non-Major
Undergraduates who are not majored in statistics but interested in an introduction to the basic principles and techniques of probability and statistics should consider STAT1011 (Introduction to Statistics) and/or STAT1012 (Statistics for Life Sciences). Students who would like to minor in Risk Management Science are required to register RMS2001 (Introduction to Risk Management Science). For details, please refer to [links] for Major/Minor Study Schemes.
- Foundation Courses
The core courses STAT2001 and STAT2006 are calculus-based introductions to probability and statistics, respectively, and are designed for students planning to continue with more advanced theory and methods courses. STAT3008 covers topics of applied regression analysis and serves as an intermediate level of statistics course. In general, 2000- and 3000-level courses are set up mainly for foundation/intermediate level study. They are usually taken by juniors/sophomores.
- Advanced Courses
Several more advanced theory and methods courses take one or more of the foundation courses as prerequisites. Among these are STAT4001 (Data Mining and Statistical Learning), STAT4002 (Multivariate Techniques with Business Applications), STAT4003 (Statistical Inference), STAT4004 (Actuarial Science), STAT4005 (Time Series), STAT4006 (Categorical Data Analysis), STAT4008 (Survival Modelling), STAT4010 (Bayesian Learning) and STAT4012 (Statistical Principles of Deep Learning with Business Applications). For RMSC majors/minors, advanced courses including RMSC4001 (Simulation Methods for Risk Management Science and Finance), RMSC4002 (Financial Data Analytics with Machine Learning), RMSC4003 (Statistical Modelling in Financial Markets), RMSC4004 (Theory of Risk and Insurance, RMSC4005 (Stochastic Calculus for Finance and Risk) and RMSC4007 (Risk Management with Derivative Concepts) are usually offered.
| Course Code | Course Title | Unit |
| STAT1011 | Introduction to Statistics |
3
|
| STAT1012 | Statistics for Life Sciences |
3
|
| STAT1013 | Data Science Toolbox |
3
|
| STAT2001 | Basic Concepts in Statistics and Probability I |
3
|
| STAT2005 | Programming Languages for Statistics |
3
|
| STAT2006 | Basic Concepts in Statistics and Probability II |
3
|
| STAT2011 | Workshop on Data Exploration and Technical Writing |
3
|
| STAT2102 | Basic Statistical Concepts and Methods II |
3
|
| STAT3001 | Foundation of Financial and Managerial Statistics |
3
|
| STAT3003 | Survey Methods |
3
|
| STAT3005 | Nonparametric Statistics |
3
|
| STAT3006 | Statistical Computing |
3
|
| STAT3007 | Introduction to Stochastic Processes |
3
|
| STAT3008 | Applied Regression Analysis |
3
|
| STAT3009 | Recommender Systems |
3
|
| STAT3011 | Workshop on Data Analysis and Statistical Computing |
3
|
| STAT3210 | Statistical Techniques in Life Sciences |
3
|
| STAT4001 | Data Mining and Statistical Learning |
3
|
| STAT4002 | Multivariate Techniques with Business Applications |
3
|
| STAT4003 | Statistical Inference |
3
|
| STAT4004 | Actuarial Science |
3
|
| STAT4005 | Time Series |
3
|
| STAT4006 | Categorical Data Analysis |
3
|
| STAT4008 | Survival Modelling |
3
|
| STAT4010 | Bayesian Learning |
3
|
| STAT4011 | Statistics Projects |
3
|
| STAT4012 | Statistical Principles of Deep Learning with Business Applications |
3
|
| STAT4013 | Practicum |
3
|
| STAT5005 | Advanced Probability Theory |
3
|
| STAT5010 | Advanced Statistical Inference |
3
|
| STAT5020 | Topics in Multivariate Analysis |
3
|
| STAT5030 | Linear Models |
3
|
| STAT5050 | Advanced Statistical Computing | 3 |
| STAT5060 | Advanced Modeling and Data Analysis |
3
|
| STAR2000 | Undergraduate Research in Science I |
1
|
| STAR2050 | Seminar I |
1
|
| STAR3000 | Undergraduate Research in Science II |
2
|
| STAR3050 | Seminar II |
1
|
| STAR4000 | Undergraduate Research in Science III |
3
|
| STAR4050 | Seminar III |
1
|
*Note: If any discrepancy arises, the version on CUSIS should be treated as the official version.
STAT1011 Introduction to Statistics
Students will learn the basic concepts of statistics, so that they will be equipped to understand statistical reports and recognize when the quantitative information presented is reliable. They will also learn about organizing and displaying graphical and numerical summaries of data, and drawing conclusions from them. The course uses computer interactive techniques, instead of tedious arithmetic calculations, to introduce simple but powerful statistical concepts. Topics include exploratory data analysis, statistical graphics, sampling variability, point and confidence interval estimation, hypothesis testing, as well as other selected topics.
STAT1012 Statistics for Life Sciences
This course introduces basic statistical concepts to life science students. Students will gain a conceptual understanding of statistical methods with the help of user-friendly software instead of complicated derivations. Topics include basic numerical and graphical descriptive statistics, basic study designs, estimation and hypothesis testing for population proportions and population means, linear regression, as well as other selected topics. Real cases in life sciences will be used to present the materials.
STAT2001 Basic Concepts in Statistics and Probability I
This course is designed to study the basic concepts of probability and statistics. Topics include elementary probability, Bayes theorem, random variables, distribution and density functions, mathematical expectation, conditional distribution, stochastic independence, correlation, special univariate and multivariate distributions, transformation of random variables, sampling distributions, law of large number, moment generating function and central limit theorem.
STAT2005 Programming Languages for Statistics
This course introduces the basic knowledge of using statistical software and programming. Students will learn programming with emphasis on data storage, data retrieval, data manipulation, data transformation, descriptive analysis, sorting, files merging, file updating, random sampling, and data reporting. Topics include basic concepts for programming, lists and objects, vectors and matrices, database manipulation, creating and retrieving dataset, printing and sorting data, exploratory data analysis, descriptive statistics, and statistical graphics, programming with statistical software, solving nonlinear equation and function optimization, simulation and Monte Carlo methods, output formats and custom report.
STAT2006 Basic Concepts in Statistics and Probability II
This course covers basic theories in estimation and testing. Topics include point estimation, interval estimation, unbiasedness, maximum likelihood estimation, hypothesis testing and likelihood ratio test.
STAT2011 Workshop on Data Exploration and Technical Writing
3 U; 3 STOT This course is designed to build students’ intuition upon data and fundamental principles of statistics, and to develop skills in technical writing. Students are required to take part in several term projects with emphasis on techniques of data exploration.
STAT2102 Basic Statistical Concepts and Methods II
This course offers basic concepts of statistics. Topics include elementary probability, Bayes theorem, random variables, distribution and density functions, discrete and continuous distributions, sampling distributions, and elementary concepts of estimation and hypothesis testing.
STAT3001 Foundation of Financial and Managerial Statistics
This course presents an overview of statistical techniques that lay the foundation for effective applications of statistics in the context of business administration. It covers financial management techniques including investment appraisal, portfolio management and models for assessing stock prices; as well as management techniques including decision analysis under uncertainty, project management, construction and usage of indices, and official statistics in Hong Kong. Selected topics in relation to the applications of multivariate techniques in marketing management will be discussed.
STAT3003 Survey Methods
This course focuses on the sample design, data analysis and interpretation of survey data. The basic sampling methods covered include simple random sampling, stratified random sampling, clustering sampling, subsampling and double sampling. The underlying method of parameter estimation will be fully discussed for simple random sampling and stratified random sampling. Special estimation techniques including ratio and regression estimations will be introduced in the context of simple random sampling. Analytic treatment of sample size determination, questionnaire design, problem of nonresponse and nonsampling errors will be discussed.
STAT3005 Nonparametric Statistics
This course introduces a wide variety of nonparametric techniques for performing statistical inference and prediction, emphasizing both conceptual foundations and practical implementation. Basic
theoretical justification is also provided. The content covers three broad themes:
(i) rank-type and order-type methods for handling location, dispersion, correlation, distribution and regression problems,
(ii) resampling-type procedures for testing and assessing precision, and
(iii) smoothing-type techniques for estimation and prediction. Topics include Wilcoxon signed-rank test, Mann-Whitney rank sum test, Spearman’s rho, Kendall’s tau, Kruskal-Wallis test, Kolmogorov-Smirnov test, bootstrapping, Jackknife, subsampling, permutation tests, kernel method, k-nearest neighbour, tree-based method, classification, etc.
theoretical justification is also provided. The content covers three broad themes:
(i) rank-type and order-type methods for handling location, dispersion, correlation, distribution and regression problems,
(ii) resampling-type procedures for testing and assessing precision, and
(iii) smoothing-type techniques for estimation and prediction. Topics include Wilcoxon signed-rank test, Mann-Whitney rank sum test, Spearman’s rho, Kendall’s tau, Kruskal-Wallis test, Kolmogorov-Smirnov test, bootstrapping, Jackknife, subsampling, permutation tests, kernel method, k-nearest neighbour, tree-based method, classification, etc.
STAT3006 Statistical Computing
The course presents model algorithms in statistical computing. It covers fundamental concepts including Monte Carlo, Markov Chain Monte Carlo, Newton’s method, EM algorithm, database accessing, elementary parallel computing. The use of related statistical packages will be demonstrated.
STAT3007 Introduction to Stochastic Processes
This course deals with applied probability and stochastic models with application in industry, engineering and management science. Topics include discrete and continuous time Markov chain, Poisson process, queueing theory, renewal process, replacement problem and elementary reliability theory.
STAT3008 Applied Regression Analysis
This course introduces the general methodology in regression analysis. Topics include least squares method in simple and multiple regression, weighted least squares, diagnostic checkings, variables selection, dummy variables and multicollinearity. A laboratory section will be held to demonstrate the use of related statistical packages and provide students opportunities for hands-on practices.
STAT3009 Recommender Systems
Commercial sites such as search engines, advertisers and media (e.g., Netflix, Amazon, Taobao) employ recommender systems for content recommendation, predicting customer behavior, compliance, or preference. This course provides an overview of predictive models for recommender systems, including content-based collaborative algorithms, latent factor models, and deep learning models, as well as Python implementation, evaluation and metrics for recommender systems. Students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:
- Recommender systems implementation and existing libraries
- Correlation-based collaborative filtering, latent factor models, neural collaborative filtering, deep learning models
- Recommender systems, link prediction, Top-K recommendation, Tuning, bagging, ensemble in recommender systems
STAT3011 Workshop on Data Analysis and Statistical Computing
3 U; 3 STOT This course is designed to strengthen students’ ability in statistical computing as well as in processing and analysing data. Students are required to participate in several term projects with emphasis on techniques of data management and analysis.
STAT3210 Statistical Techniques in Life Sciences
This course introduces statistical techniques commonly used in life sciences. Topics include descriptive statistics, parameter estimation, hypothesis testing for population proportions and population means, contingency table analysis, correlation and linear regression, logistic regression, survival analysis, and study designs.
STAT4001 Data Mining and Statistical Learning
The course presents model statistical methods for dealing high dimensional and ultrahigh dimensional complex data. It covers fundamental concepts including regularization methods, smoothing techniques, additive models, mixture models, model selection, model averaging, and ensemble learning. The use of related statistical packages will be demonstrated.
STAT4002 Multivariate Techniques with Business Applications
This course deals with multivariate statistical techniques and their applications in business. Topics are selected from multivariate normal distribution, analysis of means, profile analysis, MANOVA, partial correlation, multiple and canonical correlations, discriminant analysis, and principal components. The integrated use of these techniques in analysing business problems will be emphasized throughout the course.
STAT4003 Statistical Inference
This course provides an introduction to statistical inference. Topics include statistical models, sampling distributions, asymptotic distributions, sufficiency, maximum likelihood estimation, Bayesian estimation, Rao-Blackwell theorem, Cramér-Rao theorem and the best unbiased estimator, Neyman-Pearson lemma, uniformly most powerful test and general likelihood ratio test.
STAT4004 Actuarial Science
This course covers the basic principles of life contingencies, which is a science for life insurance. Topics include life tables, annuities, assurances, net and gross premiums, reserve, and multiple life theory.
STAT4005 Time Series
This course deals with time series with applications. Fundamental concepts of time series such as trends, stationary process, ARIMA process, model building (including parameter estimation, order determination and diagnostic checking), forecasting and seasonal models, ARCH and GARCH models will be covered. The use of related statistical packages will be demonstrated.
STAT4006 Categorical Data Analysis
This course deals with major statistical techniques in analysing categorical data. Topics include measures of association, inference for two-way contingency tables, loglinear models, logit models and models for ordinal variables. The use of related statistical packages will be demonstrated.
STAT4008 Survival Modelling
This course focuses on the modelling of survival data and its applications in actuarial and medical sciences. It covers basic concepts of lifetime distribution, various types of censoring, and methods to analyse censored data using non-parametric, parametric and semi-parametric models. Emphases are on the construction of the likelihood functions, parameter estimation and hypothesis testing.
STAT4010 Bayesian Learning
This course introduces Bayesian inference procedures and philosophy, emphasizing both conceptual foundations and computational strategies. It covers conjugate families of distributions, Bayesian credible regions, Jeffreys prior, Markov Chain Monte Carlo, Bayes factors, Bayesian information criterion, imputation, Bayesian linear-regression models, model averaging, hierarchical models and empirical Bayes models.
STAT4011 Statistics Projects
3 U; 3 STOT This course is designed to enhance students’ competence in integrating and applying statistical techniques in a practical manner. Students are required to conduct several term projects with emphasis on applications.
STAT4012 Statistical Principles of Deep Learning with Business Applications
This course introduces the basic statistics and principles behind different contemporary models in deep learning with applications in business. Emphasis is put on various topics on multilayer artificial neural networks, such as Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN) and Recurrent Neural Network (RNN), and also Reinforcement Learning (RL). About their usage, CNN brings new insights into image classification, and also helps to digitalize business information; GAN finds wide applications in speech recognition and text-mining; RNN is very useful for hand-writing recognition; reinforcement learning enables effective decision making in rapidly changing environments such as financial markets. Statistical packages including R, EXCEL and Python will be used to demonstrate these methods. Examples from financial and business contexts will be accentuated in this course. The students taking this course are expected to have acquired basic background knowledge on calculus, linear algebra, probability and statistics as prerequisites.
STAR2000 Undergraduate Research in Science I
1U Overview Long Description This is an elementary research-based course that introduces current issues and/or special important foundation topics in a science discipline. Students are required to finish one of the following two tasks: 1. Read and discuss readings assigned by the instructor; 2. Conduct elementary laboratory tasks.
STAR2050 Seminar I
1U This course aims to widen students’ scope of horizon in a broad area of scientific research. Students are required to participate in research seminars or conferences.
STAR3000 Undergraduate Research in Science II
2U This is an intermediate research-based course aiming at introducing more focused research areas in a specific science discipline. Students are required to finish one of the following two tasks: 1. Read and discuss recent research outputs assigned by the instructor; 2. Conduct research related laboratory.
STAR3050 Seminar II
1U Students are required to participate in research seminars or conferences related to their target scientific research area approved by the instructor. Pre-requisite/Co-requisite: STAR2050
STAR4000 Undergraduate Research in Science II
This is an advanced research-based course. Students are required to investigate and/or analyze in depth recent advances of a specific area in the science discipline. They are expected to conduct research that may lead to future postgraduate studies.
STAR4050 Seminar III
1U Students are required to participate in research seminars or conferences related to a specific research area. Students have to discuss possible future research on at least one of the talks. Pre-requisite/Co-requisite: STAR3050
STAT1011 Introduction to Statistics
Students will learn the basic concepts of statistics, so that they will be equipped to understand statistical reports and recognize when the quantitative information presented is reliable. They will also learn about organizing and displaying graphical and numerical summaries of data, and drawing conclusions from them. The course uses computer interactive techniques, instead of tedious arithmetic calculations, to introduce simple but powerful statistical concepts. Topics include exploratory data analysis, statistical graphics, sampling variability, point and confidence interval estimation, hypothesis testing, as well as other selected topics.
STAT1012 Statistics for Life Sciences
This course introduces basic statistical concepts to life science students. Students will gain a conceptual understanding of statistical methods with the help of user-friendly software instead of complicated derivations. Topics include basic numerical and graphical descriptive statistics, basic study designs, estimation and hypothesis testing for population proportions and population means, linear regression, as well as other selected topics. Real cases in life sciences will be used to present the materials.
STAT1013 Data Science Toolbox
This course will give a conceptual introduction, implementation, and interpretation of the data scientist’s toolbox in practice. There are three components to this course. The first is a practical introduction to the tools that will be used in the project like Python, Github, Colab, Jupyter notebook, markdown, git. The second is a conceptual introduction to A/B test and statistical predictive methods. The third is about Case Studies of A/B Test and machine learning predictive methods based on the Toolbox.
STAT2001 Basic Concepts in Statistics and Probability I
This course is designed to study the basic concepts of probability and statistics. Topics include elementary probability, Bayes theorem, random variables, distribution and density functions, mathematical expectation, conditional distribution, stochastic independence, correlation, special univariate and multivariate distributions, transformation of random variables, sampling distributions, law of large number, moment generating function and central limit theorem.
STAT2005 Programming Languages for Statistics
This course introduces the basic knowledge of using statistical software and programming. Students will learn programming with emphasis on data storage, data retrieval, data manipulation, data transformation, descriptive analysis, sorting, files merging, file updating, random sampling, and data reporting. Topics include basic concepts for programming, lists and objects, vectors and matrices, database manipulation, creating and retrieving dataset, printing and sorting data, exploratory data analysis, descriptive statistics, and statistical graphics, programming with statistical software, solving nonlinear equation and function optimization, simulation and Monte Carlo methods, output formats and custom report.
STAT2006 Basic Concepts in Statistics and Probability II
This course covers basic theories in estimation and testing. Topics include point estimation, interval estimation, unbiasedness, maximum likelihood estimation, hypothesis testing and likelihood ratio test.
STAT2011 Workshop on Data Exploration and Technical Writing
3 U; 3 STOT This course is designed to build students’ intuition upon data and fundamental principles of statistics, and to develop skills in technical writing. Students are required to take part in several term projects with emphasis on techniques of data exploration.
STAT2102 Basic Statistical Concepts and Methods II
This course offers basic concepts of statistics. Topics include elementary probability, Bayes theorem, random variables, distribution and density functions, discrete and continuous distributions, sampling distributions, and elementary concepts of estimation and hypothesis testing.
STAT3001 Foundation of Financial and Managerial Statistics
This course presents an overview of statistical techniques that lay the foundation for effective applications of statistics in the context of business administration. It covers financial management techniques including investment appraisal, portfolio management and models for assessing stock prices; as well as management techniques including decision analysis under uncertainty, project management, construction and usage of indices, and official statistics in Hong Kong. Selected topics in relation to the applications of multivariate techniques in marketing management will be discussed.
STAT3003 Survey Methods
This course focuses on the sample design, data analysis and interpretation of survey data. The basic sampling methods covered include simple random sampling, stratified random sampling, clustering sampling, subsampling and double sampling. The underlying method of parameter estimation will be fully discussed for simple random sampling and stratified random sampling. Special estimation techniques including ratio and regression estimations will be introduced in the context of simple random sampling. Analytic treatment of sample size determination, questionnaire design, problem of nonresponse and nonsampling errors will be discussed.
STAT3005 Nonparametric Statistics
This course introduces a wide variety of nonparametric techniques for performing statistical inference and prediction, emphasizing both conceptual foundations and practical implementation. Basic
theoretical justification is also provided. The content covers three broad themes:
(i) rank-type and order-type methods for handling location, dispersion, correlation, distribution and regression problems,
(ii) resampling-type procedures for testing and assessing precision, and
(iii) smoothing-type techniques for estimation and prediction. Topics include Wilcoxon signed-rank test, Mann-Whitney rank sum test, Spearman’s rho, Kendall’s tau, Kruskal-Wallis test, Kolmogorov-Smirnov test, bootstrapping, Jackknife, subsampling, permutation tests, kernel method, k-nearest neighbour, tree-based method, classification, etc.
theoretical justification is also provided. The content covers three broad themes:
(i) rank-type and order-type methods for handling location, dispersion, correlation, distribution and regression problems,
(ii) resampling-type procedures for testing and assessing precision, and
(iii) smoothing-type techniques for estimation and prediction. Topics include Wilcoxon signed-rank test, Mann-Whitney rank sum test, Spearman’s rho, Kendall’s tau, Kruskal-Wallis test, Kolmogorov-Smirnov test, bootstrapping, Jackknife, subsampling, permutation tests, kernel method, k-nearest neighbour, tree-based method, classification, etc.
STAT3006 Statistical Computing
The course presents model algorithms in statistical computing. It covers fundamental concepts including Monte Carlo, Markov Chain Monte Carlo, Newton’s method, EM algorithm, database accessing, elementary parallel computing. The use of related statistical packages will be demonstrated.
STAT3007 Introduction to Stochastic Processes
This course deals with applied probability and stochastic models with application in industry, engineering and management science. Topics include discrete and continuous time Markov chain, Poisson process, queueing theory, renewal process, replacement problem and elementary reliability theory.
STAT3008 Applied Regression Analysis
This course introduces the general methodology in regression analysis. Topics include least squares method in simple and multiple regression, weighted least squares, diagnostic checkings, variables selection, dummy variables and multicollinearity. A laboratory section will be held to demonstrate the use of related statistical packages and provide students opportunities for hands-on practices.
STAT3009 Recommender Systems
Commercial sites such as search engines, advertisers and media (e.g., Netflix, Amazon, Taobao) employ recommender systems for content recommendation, predicting customer behavior, compliance, or preference. This course provides an overview of predictive models for recommender systems, including content-based collaborative algorithms, latent factor models, and deep learning models, as well as Python implementation, evaluation and metrics for recommender systems. Students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:
- Recommender systems implementation and existing libraries
- Correlation-based collaborative filtering, latent factor models, neural collaborative filtering, deep learning models
- Recommender systems, link prediction, Top-K recommendation, Tuning, bagging, ensemble in recommender systems
STAT3011 Workshop on Data Analysis and Statistical Computing
3 U; 3 STOT This course is designed to strengthen students’ ability in statistical computing as well as in processing and analysing data. Students are required to participate in several term projects with emphasis on techniques of data management and analysis.
STAT3210 Statistical Techniques in Life Sciences
This course introduces statistical techniques commonly used in life sciences. Topics include descriptive statistics, parameter estimation, hypothesis testing for population proportions and population means, contingency table analysis, correlation and linear regression, logistic regression, survival analysis, and study designs.
STAT4001 Data Mining and Statistical Learning
The course presents model statistical methods for dealing high dimensional and ultrahigh dimensional complex data. It covers fundamental concepts including regularization methods, smoothing techniques, additive models, mixture models, model selection, model averaging, and ensemble learning. The use of related statistical packages will be demonstrated.
STAT4002 Multivariate Techniques with Business Applications
This course deals with multivariate statistical techniques and their applications in business. Topics are selected from multivariate normal distribution, analysis of means, profile analysis, MANOVA, partial correlation, multiple and canonical correlations, discriminant analysis, and principal components. The integrated use of these techniques in analysing business problems will be emphasized throughout the course.
STAT4003 Statistical Inference
This course provides an introduction to statistical inference. Topics include statistical models, sampling distributions, asymptotic distributions, sufficiency, maximum likelihood estimation, Bayesian estimation, Rao-Blackwell theorem, Cramér-Rao theorem and the best unbiased estimator, Neyman-Pearson lemma, uniformly most powerful test and general likelihood ratio test.
STAT4004 Actuarial Science
This course covers the basic principles of life contingencies, which is a science for life insurance. Topics include life tables, annuities, assurances, net and gross premiums, reserve, and multiple life theory.
STAT4005 Time Series
This course deals with time series with applications. Fundamental concepts of time series such as trends, stationary process, ARIMA process, model building (including parameter estimation, order determination and diagnostic checking), forecasting and seasonal models, ARCH and GARCH models will be covered. The use of related statistical packages will be demonstrated.
STAT4006 Categorical Data Analysis
This course deals with major statistical techniques in analysing categorical data. Topics include measures of association, inference for two-way contingency tables, loglinear models, logit models and models for ordinal variables. The use of related statistical packages will be demonstrated.
STAT4008 Survival Modelling
This course focuses on the modelling of survival data and its applications in actuarial and medical sciences. It covers basic concepts of lifetime distribution, various types of censoring, and methods to analyse censored data using non-parametric, parametric and semi-parametric models. Emphases are on the construction of the likelihood functions, parameter estimation and hypothesis testing.
STAT4010 Bayesian Learning
This course introduces Bayesian inference procedures and philosophy, emphasizing both conceptual foundations and computational strategies. It covers conjugate families of distributions, Bayesian credible regions, Jeffreys prior, Markov Chain Monte Carlo, Bayes factors, Bayesian information criterion, imputation, Bayesian linear-regression models, model averaging, hierarchical models and empirical Bayes models.
STAT4011 Statistics Projects
3 U; 3 STOT This course is designed to enhance students’ competence in integrating and applying statistical techniques in a practical manner. Students are required to conduct several term projects with emphasis on applications.
STAT4012 Statistical Principles of Deep Learning with Business Applications
This course introduces the basic statistics and principles behind different contemporary models in deep learning with applications in business. Emphasis is put on various topics on multilayer artificial neural networks, such as Convolutional Neural Network (CNN), Generative Adversarial Networks (GAN) and Recurrent Neural Network (RNN), and also Reinforcement Learning (RL). About their usage, CNN brings new insights into image classification, and also helps to digitalize business information; GAN finds wide applications in speech recognition and text-mining; RNN is very useful for hand-writing recognition; reinforcement learning enables effective decision making in rapidly changing environments such as financial markets. Statistical packages including R, EXCEL and Python will be used to demonstrate these methods. Examples from financial and business contexts will be accentuated in this course. The students taking this course are expected to have acquired basic background knowledge on calculus, linear algebra, probability and statistics as prerequisites.
STAT4013 Practicum
The course serves to provide a bridge between the classroom and the real industrial world. Students will be required to complete a project assigned by a company or an organization on a part-time basis. The student will undertake a project under the joint supervision of an instructor and a member of the company or organization. Students will be required to give a final project presentation and submit a written report, on which their assessment will be based. Advisory: For majors only.
STAR2000 Undergraduate Research in Science I
1U Overview Long Description This is an elementary research-based course that introduces current issues and/or special important foundation topics in a science discipline. Students are required to finish one of the following two tasks: 1. Read and discuss readings assigned by the instructor; 2. Conduct elementary laboratory tasks.
STAR2050 Seminar I
1U This course aims to widen students’ scope of horizon in a broad area of scientific research. Students are required to participate in research seminars or conferences.
STAR3000 Undergraduate Research in Science II
2U This is an intermediate research-based course aiming at introducing more focused research areas in a specific science discipline. Students are required to finish one of the following two tasks: 1. Read and discuss recent research outputs assigned by the instructor; 2. Conduct research related laboratory.
STAR3050 Seminar II
1U Students are required to participate in research seminars or conferences related to their target scientific research area approved by the instructor. Pre-requisite/Co-requisite: STAR2050
STAR4000 Undergraduate Research in Science II
This is an advanced research-based course. Students are required to investigate and/or analyze in depth recent advances of a specific area in the science discipline. They are expected to conduct research that may lead to future postgraduate studies.
STAR4050 Seminar III
1U Students are required to participate in research seminars or conferences related to a specific research area. Students have to discuss possible future research on at least one of the talks. Pre-requisite/Co-requisite: STAR3050