This track focuses on Machine Learning, preparing students with the technical knowledge and practical skills the field expects at the Master's level.
Graduates are equipped to pursue relevant technology career paths, with a self-paced format built for students balancing work and other commitments.
CS-044Networks and Telecommunications
Graduate-level study of networking infrastructure and telecommunications systems.
- Network infrastructure design
- Telecommunications standards
- Managing enterprise connectivity
CS-058Information Technology
Advanced study of IT systems management and strategic planning.
- IT strategy and planning
- Systems lifecycle management
- Aligning IT with business goals
CS-392Computer Science
A graduate-level review of core computer science theory and practice.
- Advanced algorithmic thinking
- Computational theory basics
- Applying CS principles at scale
CS-417Computing Concepts
Explores advanced computing concepts relevant to graduate-level practice.
- Distributed computing basics
- Computing architecture concepts
- Emerging computing paradigms
CS-605IT Project Management
Covers planning, executing, and delivering technology projects successfully.
- IT project planning fundamentals
- Managing project timelines and budgets
- Risk management in tech projects
2
Specialist Courses
5 Courses
AML-2507Advanced Machine Learning Concepts
Graduate-level study of advanced modeling techniques and architectures.
- Advanced model architectures
- Deep learning fundamentals
- Optimizing model performance
MLI-2508Machine Learning Innovations
Explores emerging trends and innovations shaping the machine learning field.
- Emerging ML research trends
- Novel model architectures
- Innovations in automated learning
MLL-2509Machine Learning Leadership
Covers leading machine learning teams and initiatives within an organization.
- Leading data science teams
- Communicating ML value to stakeholders
- Managing ML project lifecycles
MLR-2510Machine Learning Research Methods
Introduces rigorous research methods used in machine learning studies.
- Designing ML experiments
- Evaluating research reproducibility
- Reviewing ML literature critically
PPI-2511Professional Practice in Machine Learning
Covers professional and ethical standards in applied machine learning work.
- Ethical considerations in ML
- Professional ML workflow standards
- Collaborating across ML project teams
$60,000
Total Program Cost
Fees are billed per term and are subject to change. Financial aid, scholarships, and flexible payment plans are available to students who qualify.
Explore Financial Aid
Admission requires Bachelor's Degree or equivalent.
01
Submit Your Application
Complete the online application with your basic and academic details.
An admissions advisor reviews your application and any transfer credit.
Receive your decision, complete enrollment, and start your first term.
Apply Now