Online MS in Computer Science with Artificial Intelligence Focus Area
Create Intelligent Systems That Solve Real Problems
Format
100% Online
Credits
Core: 18 (5 courses and capstone)
Concentration: 9 (3 required courses)
Elective: 3 (1 course)
Duration
1.5 years, full time; 2 years, part time
Start Date
January 25, 2027
Application Deadline
January 18, 2027
Artificial Intelligence Concentration Overview
Artificial intelligence is reshaping organizations across every sector. For computer science professionals, the possibilities in this field are nearly endless. That’s why the Seidenberg School of Computer Science and Information Systems at Pace University has put together a specialized concentration to prepare students to take advantage of this opportunity.
The Artificial Intelligence concentration gives you the applied skills to meet market demand and innovate using AI. You’ll work with machine learning models, deep learning frameworks, and intelligent systems, giving you the skills to build and monitor AI systems.
Get Started
Request
Information
To learn more about the online Master of Science in Computer Science program, fill out the fields in this form to download a free brochure. If you have any questions at any time, please contact an enrollment specialist at (914) 758-1080.
Core Skills You’ll Master
Through this concentration, you’ll gain hands-on expertise in:
- Artificial intelligence models and capabilities
- Machine learning and deep learning
- Python programming
- Grid and cloud computing
- Computing theory
- Internet and web computing
- Database systems
Where This Concentration Takes You
Whether you’re advancing as a software engineer or specializing in machine learning, this concentration prepares you for technical roles that build and deploy intelligent systems. According to Lightcast labor market data, professionals in this field can pursue roles like:
Artificial Intelligence Concentration Curriculum
Below is an outline of what your master’s in computer science might look like if you choose an AI focus area. For comprehensive curriculum information, see our curriculum page.
Core Requirements (15 Credits)
This course provides an integrated survey of fundamental ideas in the areas of computer architecture; operating systems; and programming language specification and translation. The focus of this course is on the computer organization of a computer system, including the processor architecture and the memory system. In particular, we will discuss the internal representation of information, instruction set architectures and implementation techniques for computer arithmetic, control path design, and pipelining. We examine the hardware and software components required to go from a program expressed in a high-level programming language like C to the computer actually running the program. This course takes a bottom-up approach to discovering how a computer works, and introduces basic concepts of operating systems, compilers and interpreters.
This course includes the following: applications of abstraction and divide-and-conquer in computer science (hardware, software, theory), essential algorithms including searching, sorting, hashing and graphs; popular algorithms such as string machine, Map Reduce and RSA and their applications, complexity, computability, NP-hard problems, NP-complete problems, and undecided problems, and finite state automata vs. regular expressions.
This course focuses on the following: parallel computing theory, Parallel Random-Access Machines (PRAMs), Amdahl’s law for theoretical speedup limits, Petri Nets, parallel vs. distributed computing, speedup, fault-tolerance, resource-sharing, parallel architectures, data flow, instruction-level pipelining, embedded multicore systems, shared-memory multiprocessors, distributed-memory multicomputers, interconnection networks, distributed systems: client-server systems, cluster computing, computing grids, cloud computing, parallel and distributed programming with industry standard MPI (Message Passing Interface), and parallel algorithms.
This course focuses on the integrated hands-on coverage of fundamental concepts and technologies for enterprise and Internet computing. Topics include data storage, XML data specification, parsing and validation, data and language translation, networking and Web technology overview, software framework technology for controlling software system complexity, and a roadmap for the enterprise computing technologies. CS612 students need a Windows computer to complete course projects.
This course includes the following topics: database management system installation and configuration, database’s role as a middleware in system hierarchy, Entity Relationship (E-R) model for logical design, schema normalization and performance tradeoffs, database management with SQL through database console, database programming through JDBC, event-processing with triggers, efficient data processing with stored-procedures, transactions management and ACID properties, database security, and crash recovery.
Artificial Intelligence Focus (9 Credits)
This course focuses on theory and data structures and algorithms related to artificial intelligence and heuristic programming. Topics include description of cognitive processes, definition of heuristic vs. algorithmic methods, state space and problem reduction, search methods, theorem proving, natural language processing, and pattern recognition techniques.
This course introduces students to the Python programming language with an emphasis on Python’s data analytics libraries. Students will learn the fundamentals of Python and key modules including: scipy, numpy, scikit-learn, pandas, statsmodels, and matplotlib. The course covers basic language syntax, object types, variables, reading data from files, and writing to files. Building on these concepts, students will create functions, and learn how to control program flow. Students will use Python to clean and prepare data, conduct exploratory data analysis, and build predictive models.
Students will learn how to design, implement, and evaluate a pipeline for supervised classification of structured data, using a variety of Machine Learning techniques (e.g., Logistic Regression). Apply Deep Learning techniques (e.g., Convolutional Neural Networks, Recurrent Neural Networks) to classify unstructured data, including images and text. Describe important considerations for applying Machine Learning in practice.
Elective (3 Credits)
AI focus area students will choose one additional elective of their choice.
Capstone Project (3 Credits)
Students choose either a project (3 credits) or a thesis (6 credits). If AI focus area students choose a thesis, they are not required to take an additional elective.
Build the AI Skills Employers Can’t Find
82% of enterprise leaders say their organization offers AI training. 59% still report a skills gap (DataCamp, 2026). Training isn’t the problem, depth is. The Pace AI concentration gives you the technical foundation to build and implement AI systems.
Job postings that mention AI skills have salaries that are 28% higher than postings that do not.
Frequently Asked Questions
An online MS in Computer Science gives you technical skills to take on AI challenges and solve problems directly, as well as theoretical knowledge to understand how systems work. With a focus area in AI, you can take that expertise even further and build the algorithms and machine learning models that power artificial intelligence.
Full-time students can complete the online master’s in computer science program in as little as 1.5 years. Some students choose to complete the degree on a part-time schedule so they can continue to work full time.
Computer science is about developing new software solutions and technologies to empower businesses efficiency and societal improvement. Information technology emphasizes the deployment, integration, management and security of business technologies. Information systems professionals use existing computing technologies to support efficient business operations and maximize profits.
Computer science professionals are the software engineers who work behind the scenes, empowering IT and IS professionals to do their work. Without computer science graduates, other technology professionals would not have sufficient software to do their jobs effectively.
Not Sure Artificial Intelligence Is Your Path?
The Pace online MS in Computer Science offers a concentration in application development and the option to create your own concentration.
Develop software for web and mobile platforms using cutting-edge programming practices, then learn how to test and maintain applications that scale for game-changing results.
Contact Us
Phone Number
Ready To Learn More?
Download our program brochure, and explore our curriculum, admissions, and FAQ pages.