12 courses with the subject MSCS, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
MSCS 7000Program Design3
This course introduces the fundamental principles and practices of software design and architecture. Students will learn how to apply the Universal Modeling Language (UML) to model a program. In addition, students will learn how to work with users to determine requirements that can be translated into an application. Students will learn how to use design patterns, principles, and architectures to create reusable and flexible software applications and systems. The course covers topics such as object-oriented design, software architecture, and visual notation for documenting design and architecture.
This course explores advanced topics in data structures and algorithms, building on foundational knowledge from undergraduate courses to explore complex and efficient solutions for real-world problems. Students learn advanced complex uses for data structures such as B-trees, Fibonacci heaps, lists, tables, trees, heaps, and graphs, as well as sophisticated algorithmic techniques like dynamic programming, greedy algorithms, and randomized algorithms. The course emphasizes both theoretical understanding and practical implementation, preparing students to design and analyze efficient algorithms for large-scale applications.
This course introduces the core concepts, techniques, and applications of artificial intelligence (AI). It covers foundational topics such as search algorithms, knowledge representation, logic and reasoning, machine learning, and natural language processing. Students will learn how to apply AI methodologies to solve real-world problems throughout the course. The course culminates in a case study project, where students will analyze and apply AI techniques to problems like healthcare, finance, robotics, or software engineering.
This course provides an in-depth understanding of software project management principles and practices. It focuses on the application of project management techniques specifically tailored to software development projects. Students will learn how to plan, execute, and control software projects, ensuring they are completed on time, within budget, and to the required quality standards.
of Experiential Education YEAR 1 (TERM 3) Session I: MSCS 7200 Computer Vision Term II: MSCS 7210 Natural Language Processing 1 credit of Experiential Education YEAR 2 (TERM 1) Session I: MSCS 7220 Human Centered AI Design Session II: MSCS 7230 Machine Learning 1 credit of Experiential Education YEAR 2 (TERM 2) Session I and Session II: Experiential Education in Person 2 credits of Experiential Education *Note: 1. MSCS 7010 Internship experience is required and offered every semester. 2. For those students unable to obtain an internship, the required six credits of experiential education can be earned through the Capstone Project (MSCS 7610) or Thesis (MSCS 7650) in Year 2, Term 2. Master of Arts in Criminal Justice (MACJ) Grad
MSCS 7210Natural Language Processing and Large Language Models3
This course explores the field of Natural Language Processing (NLP) with a particular focus on modern techniques, including large language models (LLMs) such as transformers and their applications. Students will gain an understanding of foundational NLP concepts like text processing, language modeling, and sequence- to-sequence models, progressing to advanced topics such as transfer learning, fine-tuning pre-trained language models, and prompt engineering. Through hands-on exercises and a case study, students will apply NLP techniques to solve real-world problems in areas such as sentiment analysis, text summarization, chatbots, and machine translation.
In this course, students will investigate new developments in the area of human-AI interaction (HAI). They will examine the unique interface design challenges presented by AI and how to design and develop AI-driven human-centered intelligent interaction technologies while critically assessing their social and ethical impact. Students will learn to read, interpret, and discuss crucial research papers and complete a research project to develop a holistic understanding of the human-AI interaction landscape, including its challenges, opportunities, and ethical considerations.
This course focuses on how machines can autonomously enhance their capabilities through experience. Some sample applications include facial recognition software, recommender systems, and autonomous robots. Students will explore both theoretical frameworks and practical algorithms from multiple viewpoints. The course covers theory and algorithms related to supervised learning (generative/discriminative learning, parametric/non- parametric learning, neural networks, support vector machines), unsupervised learning (clustering, dimensionality reduction, kernel methods), learning theory, reinforcement learning, and adaptive control.
Students will work with their faculty advisor to select a significant computer science application for this individual project. The application could be for an external client. Students will study the underlying problem domain and relevant technologies, design, implement, test the application, and present the project as a report, oral presentation and demonstration of the application. The advisor may decide to substitute a report with alternative project documentation and/or relevant user guides.
