M.B.A. in Business Administration
The Stevens MBA program offers various formats to cater to diverse student needs, inclu...
M.S. in Machine Learning
Hoboken, New Jersey
INTAKE: Jan & Sept
Program Overview
The M.S. in Machine Learning is a 30-credit-hour program, typically completed within 1.5 to 2 years for full-time students. It is meticulously designed to immerse students in the fundamentals of machine learning theory and application. The curriculum is structured around four core machine learning courses, three machine learning core electives, and three general electives, providing a balanced approach to theoretical understanding and practical skill development. The program is available both on-campus and fully online, offering flexibility for a diverse range of students, including working professionals.
STEM-designated: Yes, the Stevens Institute of Technology M.S. in Machine Learning program is STEM-designated. This is a significant advantage, particularly for international students. The STEM designation makes them eligible for a 24-month extension of their Optional Practical Training (OPT) in the United States, allowing for a total of up to three years of valuable post-graduation work experience. This designation clearly reflects the program's rigorous quantitative, scientific, and technological focus, which is highly valued by employers in the machine learning and AI industries.
Curriculum: The 30-credit curriculum provides a comprehensive deep dive into machine learning. Core requirements (12 credits) often include courses such as Artificial Intelligence, Machine Learning: Fundamentals and Applications, Statistical Machine Learning, Deep Learning, and Natural Language Processing. Students then select three machine learning core electives (9 credits) from a range of specialized topics like Knowledge Discovery and Data Mining, 3D Computer Vision, Health Informatics, Mathematical Foundations of Machine Learning, Causal Inference, Text Mining and Information Retrieval, Augmented Intelligence and Generative AI, and Big Data Technologies. The remaining general electives (9 credits) allow for further specialization or broader exploration of related graduate courses. The program emphasizes hands-on experience and implementation using programming languages like Python and popular deep learning frameworks.
Research Focus: The M.S. in Machine Learning program at Stevens has a strong research focus, aiming to keep its graduates at the forefront of progress in machine learning and related disciplines. The Department of Computer Science, which houses the program, conducts extensive research in AI and Machine Learning, including causal inference, large-scale and high-dimensional data analysis, knowledge representation and reasoning, time series data, uncertainty, deep learning, and natural language processing. This research is applied to diverse domains such as health informatics, social science, and image processing. The Stevens Institute for Artificial Intelligence (SIAI), an interdisciplinary center, further amplifies this research, engaging over 100 faculty members from various academic units in AI and machine learning research, often in collaboration with industry and government partners to solve complex global problems.
Industry Engagement: Stevens Institute of Technology places a very high priority on industry engagement for its Machine Learning program. Its strategic location in Hoboken, New Jersey, directly across from New York City's thriving tech scene, offers unparalleled access to over 7,500 tech companies, including industry leaders like Google, Facebook, Amazon, Microsoft, and Bloomberg, as well as a vibrant startup ecosystem. The curriculum is continuously updated to reflect the latest industry trends, such as generative AI, ensuring graduates acquire in-demand skills. Stevens boasts an exceptional 100% employment rate within three months of graduation for its Machine Learning Class of 2023, with a mean compensation of $121,000, underscoring the strong industry demand for its graduates and the program's effective career preparation.
Global Perspective: Stevens Institute of Technology fosters a global perspective within its M.S. in Machine Learning program through its diverse student population and the universal applicability of machine learning technologies. The program attracts students from various countries, creating a multicultural learning environment that enriches classroom discussions with diverse viewpoints and approaches to global challenges in AI. The skills acquired, from understanding theoretical foundations to applying models in diverse areas like bioinformatics and weather prediction, are highly transferable across international markets and industries. This global outlook, coupled with the program's robust technical foundation, prepares graduates to contribute to machine learning advancements and address complex problems on an international scale.
Location
Hoboken, New Jersey
Score
IELTS 6.5
Tuition Fee
USD 46048
Postgraduate Entry Requirements
Academic Qualifications: Applicants for postgraduate programs typically require a minimum academic achievement of 70% or above in their bachelor's degree.
English Language Proficiency:
- IELTS: Overall band score of 6.5 or 7.0 with a minimum of 6.0 in each component.
- TOEFL: Overall score of 74 or higher.
- DET (Duolingo English Test): Minimum score of 110.
Stevens Institute of Technology offers a variety of scholarships specifically designed to support international students, helping to make a world-class education more affordable and accessible. These scholarships recognize academic excellence, leadership potential, and contributions to the campus community.
Merit-Based Scholarships: Stevens provides competitive merit scholarships for international undergraduate and graduate students based on academic performance, standardized test scores, and other achievements. These awards can significantly reduce tuition costs and are automatically considered during the admissions process for many programs.
Need-Based Financial Aid: While limited, some need-based aid options are available to international students. Applicants are encouraged to provide detailed financial information to be considered for such assistance.
Graduate Fellowships and Assistantships: International graduate students may be eligible for fellowships, research assistantships, or teaching assistantships, which offer tuition waivers and stipends. These opportunities not only provide financial support but also valuable hands-on experience in research and academic work.
Special Scholarships: Stevens occasionally offers specialized scholarships targeting students from certain countries, underrepresented fields, or those pursuing specific disciplines like engineering, business, or cybersecurity. Prospective students should check the official Stevens website or contact the admissions office for current scholarship opportunities.
A Master of Science (M.S.) in Machine Learning from Stevens Institute of Technology, situated in Hoboken, New Jersey, is a highly specialized and STEM-designated program that stands at the forefront of preparing graduates for the rapidly expanding field of machine learning and its diverse applications.
Machine Learning Engineer: This is a core role for graduates, involving the design, development, and deployment of machine learning models and systems. They work on building intelligent applications, optimizing algorithms, and integrating ML solutions into various products and services.
Data Scientist: Leveraging their strong foundation in statistical machine learning, deep learning, and data analysis, graduates can analyze large datasets, identify patterns, build predictive models, and extract actionable insights to inform business strategies across industries.
AI Engineer: With a comprehensive understanding of artificial intelligence, graduates design and implement AI-powered solutions, often focusing on intelligent software development, automation, and AI integration into complex systems.
Research Scientist (ML/AI): For those interested in advancing the field, this role involves conducting cutting-edge research, developing new algorithms, and exploring novel applications of machine learning and artificial intelligence in corporate R&D labs, government agencies, or academic institutions.
Computer Vision Engineer: Specializing in the development of algorithms and systems that enable computers to understand and interpret visual data. This is crucial for applications in autonomous vehicles, facial recognition, medical imaging, and augmented reality.
Natural Language Processing (NLP) Engineer: Focusing on the interaction between computers and human language, NLP engineers develop systems for tasks like text analysis, sentiment analysis, language translation, and building conversational AI (chatbots, voice assistants).
Robotics Engineer (AI/ML focused): Graduates apply machine learning to enhance robotic systems, working on aspects like robot perception, navigation, decision-making, and learning from experience in fields such as manufacturing, logistics, and exploration.
Deep Learning Engineer: With specialized knowledge in neural networks and deep learning frameworks, these engineers build and optimize complex deep learning models for tasks such as image recognition, speech processing, and advanced pattern recognition.
Quantitative Analyst (Quant) with ML Expertise: In the financial sector, graduates can apply machine learning models to analyze market data, predict trends, develop trading strategies, and manage risk, particularly in areas like algorithmic trading and fraud detection.
Applied ML Scientist (Domain-Specific): Given the program's flexibility and potential for specialization, graduates can focus on applying machine learning within specific industries such as Health Informatics (smart health, biomedical engineering), Bioinformatics (genomics, drug discovery), or Climate Modeling (weather prediction, environmental analytics).
