| 1440581 |
Regression Modelling |
3 |
Prerequisite: 1440281 or equiv
Regression Modelling is a course in applied statistics that studies the use of linear regression techniques for examining relationships between variables. The course emphasizes the principles of statistical modelling through the iterative process of fitting a model, examining the fit to assess imperfections in the model and suggest alternative models, and continuing until a satisfactory model is reached. Both steps in this process require the use of a computer: model fitting uses various numerical algorithms, and model assessment involves extensive use of graphical displays. The R statistical computing package is used as an integral part of the course. |
| 1440582 |
Introduction to Bayesian Data Analysis |
3 |
Prerequisite: 1440281 or equiv
The Bayesian approach to statistics assigns probability distributions to both the data and unknown parameters in the problem. This way, we can incorporate prior knowledge on the unknown parameters before observing any data. Statistical inference is summarized by the posterior distribution of the parameters after data collection, and posterior predictions for new observations. The Bayesian approach to statistics is very flexible because we can describe the probability distribution of any function of the unknown parameters in the model. Modern advances in computing have allowed many complicated models, which are difficult to analyze using 'classical' (frequentist) methods, to be readily analyzed using Bayesian methodology. The aim of this course is to equip students with the skills to perform and interpret Bayesian statistical analyses. The first part of the course is devoted to describing the fundamentals of Bayesian inference by examining some simple Bayesian models. More complicated models will then be explored, including linear regression and hierarchical models in a Bayesian framework. Bayesian computational methods, especially Markov Chain Monte Carlo methods will progressively be introduced as motivated by the models discussed. Emphasis will also be placed on model checking and evaluation. |
| 1501565 |
Data Mining |
3 |
Prerequisite: Introduction to Database Management Systems (1501263), or 1501567 or equivalent.
Data mining has become one of the most interesting and rapidly growing fields. Data mining techniques are used to uncover hidden information, such as patterns, in databases and perform predictions. The data to be mined may be complex data including multimedia, spatial, and temporal. Topic include data processing, association rules, clustering, and classification. This course is designed to provide graduate students with a solid understanding of data mining concepts and tools. |
| 1501566 |
Foundations of Data Science |
3 |
Prerequisite: Introduction to Database Management Systems (1501263) or 1501567 or equivalent, and Introduction to Probability and Statistics (1440281)
Data science is an interdisciplinary field that provides tools to extract insights from data in various forms, either structured or unstructured. Data science course provides the theories, strategies, and tools to understand and apply the following topics: data preparation, data cleaning & integration, data analysis, classification, clustering, text analysis, and visualization. |
| 1501590 |
Research Methodology |
3 |
Prerequisite: 1501215-Data Structures or equivalent, and Graduate Standing
This course introduces graduate students to the practice of research. The course preliminary introduces students to concepts of research methods in data science, data resources, data collection, and literature review. The course ensure that students learn how to select a research topic, devise research questions, and plan the research. Additionally, the students will gain practical knowledge on technical writing, such as writing a thesis proposal, a survey paper, and technical review of research papers. Students will also gain the skills, and practice, of technical presentations of scientific research papers and proposal. Lab sessions will be conducted to train students on a Latex editor for writing technical reports. |
| 1440583 |
Graphical data analysis |
3 |
Prerequisite: 1440281 or equiv
This course introduces the principles of data representation, summarization and presentation with particular emphasis on the use of graphics. The course will use the R Language in a modern computing environment. |
| 1440584 |
Applied Time Series Analysis |
3 |
Prerequisite: 1440281 or equiv
This course considers statistical techniques to evaluate processes occurring through time. It introduces students to time series methods and the applications of these methods to different types of data in various contexts (such as actuarial studies, climatology, economics, finance, geography, meteorology, political science, risk management, and sociology). Time series modelling techniques will be considered with reference to their use in forecasting where suitable. While linear models will be examined in some detail, extensions to non-linear models will also be considered. The topics will include: deterministic models; linear time series models, stationary models, homogeneous non-stationary models; the Box-Jenkins approach; intervention models; non-linear models; time-series regression; time-series smoothing; case studies. Statistical software R will be used throughout this course. Heavy emphasis will be given to fundamental concepts and applied work. Since this is a course on applying time series techniques, different examples will be considered whenever appropriate. |
| 1440586 |
Design of Experiments |
3 |
Prerequisite: 1440281 or equiv
This course covers the statistical design of experiments for systematically examining how a system functions. Topics covered will include: introduction to experiments, completely randomized designs, blocking designs, factorial designs with two levels, fractional designs with two levels and response surface designs. |
| 1440589 |
Nonparametric Inference of Statistics |
3 |
Prerequisite: 1440281 or equiv
This course is an introduction to nonparametric function estimation. Topics include kernels, local polynomials, Fourier series, spline methods, wavelets, automated smoothing methods, cross-validation, large sample distributional properties of estimators, lack-of-fit tests, semiparametric models, recent advances in function Estimation. |
| 1440590 |
Stochastic Processes |
3 |
Prerequisite: 1440281 or equiv
Many systems evolve over time with an inherent amount of randomness. The purpose of this course is to develop and analyze probability models that capture the salient features of the system under study to predict the short and long term effects that this randomness will have on the systems under consideration. The study of probability models for stochastic processes involves a broad range of mathematical and computational tools. This course will strike a balance between the mathematics and the applications. |
| 1440593 |
Topics in Statistics |
3 |
Prerequisite: 1440281 or equiv
This course cover selected topics in statistical methods related to statistical analysis. It gives a brief review of linear regression models, generalized and linear mixed models. Some matrix algebra that helps to understand the quadratic forms and multivariate random variables and their distribution. In addition, some topics form the analysis of variance will be covered. The topics covered are typically not included in other statistical courses at the master's level. The course material may change (and likely will) each semester depending on Students' preferences. |
| 1501668 |
Big data & Data Analytics |
3 |
Prerequisite: : Introduction to Database Management Systems (1501263) or 1501567 or equivalent
Big data is becoming one of the most important technology that enables organizations to store, manage, and manipulate vast amounts of data efficiently to gain business insights. Big data course provides the fundamentals, technologies, and tools to understand and apply the following the Big Data analytics. Topics covered are: Big Data types, technologies, analytical tools, numerical, textual, image and stream analysis, and applications of spatial data and remote sensing. |
| 1501761 |
Topics in Data Analytics and Cloud Computing |
3 |
Prerequisite: Graduate Standing
Cloud Computing enables big data processing at a large scale, by allowing a ubiquitous access to a large number of shared remote servers, often, over the internet. This course presents advanced research topics in cloud systems, data processing frameworks, and networking. Example topics include the architecture of cloud data centers, state-of-the-art data processing frameworks, Cloud workload characteristics, and resource management and scheduling. |
| 1501764 |
Topics in Data Science |
3 |
Prerequisite: 1501263 or 1501567 or equiv
The student has to undertake and complete a research topic in Data Science under the supervision of a faculty member. The thesis work should provide the student with in-depth perspective of a particular research problem in his chosen field of specialization of Data Science. It is anticipated that the student be able to carry out this theoretical research and required practical work fairly independently under the direction of the supervisor. The student is required to submit a final thesis documenting the research, experiments, findings and defend the work in front of a committee. |
| 1501530 |
Advanced Artificial Intelligence |
3 |
Prerequisite: Graduate standing
This course covers fundamental and advanced concepts and techniques in the field of artificial intelligence. The main topic list includes intelligent agents, uninformed and informed search, adversarial search, constraint satisfaction problem, and uncertain knowledge and reasoning, which covers Bayesian Networks, and Decision Networks. In addition, advanced topics will include machine learning, reinforcement learning, natural language processing (or vision/robotics), and deep learning. |
| 1501531 |
Machine Learning |
3 |
Prerequisite: (1440211-Linear Algebra + 1501215-Data Structures) or equivalent
This course provides a broad introduction to machine learning. Main topics include Regression, classification, and clustering. Detailed subjects are simple and multiple, Ridge, kernel feature, feature selection & Lasso; Linear classifiers & logistic regression; decision trees and ensemble learning, support vector machines, and artificial neural networks. Besides, best practices in machine learning such as overfitting/regularization and bias/variance theory shall be covered. Students will learn how to identify and implement appropriate machine learning algorithms for a variety of problems. |
| 1501511 |
Advanced Programming |
3 |
Prerequisite: Basic programming course
This course aims to familiarize students with advanced methods in programming using Python, including object-oriented programming, parallel programming, data structures, algorithms, and applications of Python programming to AI and Data Science. The course covers the following major modules: (I) Object-Oriented Programming, (II) Data Structures and Algorithms, (III) Parallel Programming, (IV) Applications and Computing Tools: Features, Libraries, agents and controllers, and Regular Expressions applied to NLP tasks. |
| 1501763 |
Information Retrieval |
3 |
Prerequisite: : Introduction to Database Systems (1501263) or 1501567 or equiv
The student has to undertake and complete a research topic in Data Science under the supervision of a faculty member. The thesis work should provide the student with in-depth perspective of a particular research problem in his chosen field of specialization of Data Science. It is anticipated that the student be able to carry out this theoretical research and required practical work fairly independently under the direction of the supervisor. The student is required to submit a final thesis documenting the research, experiments, findings and defend the work in front of a committee. |
