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.

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.

This course discusses concepts, theories, and techniques for web services, service oriented computing and services science. Examines architectures for Web applications based on the classic publish, find, and bind triangle. Key topics include semantics, transactions, Web service composition and the concept of self-managing.

This course focuses on the design, development, and study of interactive visualization techniques for the analysis, comprehension, exploration, and explanation of large collections of datasets. Topics covered include principles of visual perception, data and task abstractions, visual encodings of data, interaction methods, visual analytics, and evaluation techniques. Students get hands-on experience through projects. Prerequisite: Permission by 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.

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 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 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.

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.

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.

This course introduces fundamental data engineering principles and techniques for end-to-end data preparation and management in machine learning. Students will learn to address common data challenges (e.g., noisy, imbalanced, or insufficient data) and optimize datasets for machine learning. Prerequisites: Basic knowledge of programming (e.g., Python or R) and basic probability and statistics.

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.