Courses

A student may count Directed Readings towards the credits required for the degree, subject to the permission of the graduate program director and the Instructor who will be directing the course. Topics will depend on student and Instructor interests but will not cover the same material as other MAIST courses offered in the same year. Prerequisites: Permission of the Graduate Program Director and Instructor.

A student may count Directed Readings towards the credits required for the degree, subject to the permission of the graduate program director and the Instructor who will be directing the course. Topics will depend on student and Instructor interests but will not cover the same material as other MAIST courses offered in the same year. Prerequisites: Permission of the Graduate Program Director and Instructor.

Business analytics, the use of data-driven methodologies to support decisions, plays a central role in organizations. Students will learn techniques to perform business analytics. Specifically, the tools will include advanced methods such as Artificial Intelligence, Machine Learning, Data Mining, and Process Mining. The students will analyze real business cases and will get hands-on experience through projects and case analyses. Prerequisites:ITEC1620(or similar programming course), SC/MATH 2565(or similar statistics course);Permission by instructor to wave prerequisites.

Introduces advanced techniques and core technologies used in information retrieval and studies the theory, design, and implementation of text-based information retrieval systems. Focuses on effectively interpreting imprecise queries and providing a high quality response to them from large text-based collections. Prerequisite: AP/ITEC 4020 3.00 or equivalent.

This course covers design principles and techniques of advanced information management systems such as relational database systems and big data platforms. Both classic data management issues and emerging topics in big data management are discussed. Programming projects are required. Prerequisites: Knowledge equivalent to an undergraduate database course such as AP/ITEC 3220 3.00 or LE/EECS 3421 3.00 would be desirable but is not mandatory.

This course introduces Machine Learning (ML) and discusses its models (supervised and unsupervised) and their real-life applications. This course will cover different topics of ML including linear regression, decision trees, neural networks, Naïve Bayes algorithm, association rules, deep learning, face recognition, and ML applications. It will also identify different best practices to enhance the ML models' performance (hyperparameter tuning, bias/variance concept¿). PREREQUISITES: Students should have some skills in Programming (i.e. AP/ITEC 1610 3.00, AP/ITEC 1620 3.00, AP/ITEC 2610 3.00, AP/ITEC 2620 3.00), understanding of algorithms, and problem-solving and knowledge of some basics of linear algebra and statistics (i.e. SC/MATH 1013/4, SC/MATH 1021, SC/MATH 1131 or SC/MATH 2560 or SC/MATH 2565, SC/MATH 2030 or SC/MATH 2930). Permission from the instructor(s) might be required for prerequisites.

Provides a foundation in scientific inquiry applied to both practical and theoretical IT-related problems. Students formulate research questions, select appropriate research design to collect and analyze data, prepare reports, and evaluate research proposals and projects. Students must complete this course during their first-year in the program or take this course later with Graduate Program Director's approval.

Introduces students to advanced techniques for designing and building e-business applications, exposing students to core technologies for analyzing, designing and implementing e-business applications. Students develop an understanding of how these core technologies can be applied to solve real-world problems. Prerequisite: AP/ITEC 4020 3.00 or equivalent.

Introduces students to the idea of optimal solutions. A survey of selected topics in operations research (OR) is provided emphasizing on practical applications rather than on the mathematical properties as well as on their integration into information systems. Students engage in term-long projects and conduct an in-depth study of a topic through readings and paper reviews. Prerequisite: AP/ITEC 4030 or by permission of the instructor.

This course offers an introduction to the field of financial technologies with a strong emphasis on blockchain and cryptocurrency technologies and platforms. Following a short overview of disruptive use of information technologies in the financial sector, the course proceeds with a technical introduction to blockchain platforms and networks and smart contracts. Students engage in term long projects at various levels of technical depth. Prerequisite: AP/ITEC5210/4010 or permission by instructor.

The course introduces students to computational techniques and their applications to social science. This course equips students with knowledge and skills on how computational techniques derive insights about human behavior and society from digital data. Students will gain practical experiences of applying computational techniques to addressing social questions through collecting, processing, and analyzing large-scale data using the R programming language.

Introduces emerging and hot topics in information technology discussed in the research literature. Topics will rotate annually and will focus on a specific area of interest to the instructor that is not covered in existing courses. Proposed topics include information systems security, service-oriented architecture, management of IT, web services. Prerequisite: None.