MS Applied Machine Learning

2 Years On Campus Masters Program

University of Maryland College Park

Program Overview

The University of Maryland's Master of Science in Applied Machine Learning prepares students to develop intelligent systems and solve complex real-world problems using advanced machine-learning techniques. The program combines mathematical foundations, statistical analysis, data science and computer programming with specialized study in deep learning, computer vision, natural language processing and big data technologies.

Curriculum Structure

Year 1:
Students establish a strong foundation in machine learning, statistics, data science and computational methods through six core courses. Key subjects include MSML601 – Probability and Statistics, MSML602 – Principles of Data Science and MSML603 – Principles of Machine Learning. They also study MSML604 – Introduction to Optimization, MSML605 – Computing Systems for Machine Learning and MSML606 – Algorithms and Data Structures for Machine Learning, developing the mathematical and computational skills required to build and evaluate intelligent models.

Year 2:
Students deepen their expertise through four specialized electives selected from the program's approved course offerings. Options include MSML612 – Deep Learning, MSML640 – Computer Vision and MSML641 – Natural Language Processing, allowing students to explore neural networks, image recognition and language-processing technologies. Additional electives include MSML642 – Robotics, MSML650 – Cloud Computing and MSML651 – Big Data Analytics, providing opportunities to apply machine-learning techniques across different industries and technological environments.

Focus Areas

Applied Machine Learning, Deep Learning, Probability and Statistics, Data Science, Optimization, Computer Vision, Natural Language Processing, Robotics, Cloud Computing, Big Data Analytics, Algorithms and Data Structures, Computational Systems and Intelligent Data Analysis.

Learning Outcomes

Apply mathematical and statistical methods to machine-learning problems, develop and evaluate supervised and unsupervised learning models, implement optimization techniques, design efficient algorithms and data structures, understand computing systems used for machine learning, build deep-learning applications, analyze visual and textual data, apply machine learning to robotics and big data environments and develop computational solutions for complex real-world challenges.

Professional Alignment (Accreditation)

The program is offered through the University of Maryland's College of Computer, Mathematical, and Natural Sciences and emphasizes practical and theoretical machine-learning competencies. Its curriculum integrates statistics, algorithms, optimization, programming and advanced computational methods, preparing students for technical roles in artificial intelligence, data science and machine learning. The program follows a coursework-based structure with no thesis requirement. No separate professional accreditation is specified for the M.S. in Applied Machine Learning in the official university catalog.

Reputation (Employability Rankings)

The University of Maryland, College Park is a major public research university recognized for its academic strengths in computing, mathematics and scientific research. Its College of Computer, Mathematical, and Natural Sciences provides students with an interdisciplinary environment for exploring advanced computational technologies and data-driven research. The program's emphasis on machine learning, deep learning, computer vision and big data prepares graduates for opportunities as machine-learning engineers, AI developers, data scientists, research analysts, computer vision engineers and computational specialists.

Experiential Learning (Research, Projects, Internships etc.)

At the University of Maryland College Park, the MS in Applied Machine Learning program is designed to provide students with hands-on experience that is crucial for mastering the complexities of machine learning. Students engage in practical projects that simulate real-world challenges, allowing them to apply theoretical knowledge in a collaborative environment. The program leverages state-of-the-art facilities and tools, ensuring that students are well-equipped to tackle industry demands.

Here are some key aspects of the experiential learning opportunities available in the program:

- Research Labs: Access to specialized labs such as the Maryland Cybersecurity Center (MC2) and the Institute for Advanced Computer Studies (UMIACS), where students can work on cutting-edge research projects.
- Group Projects: Collaborative projects that encourage teamwork and problem-solving, often involving real datasets and industry partnerships.
- Internships: Opportunities to intern with leading tech companies and organizations, providing invaluable industry experience and networking opportunities.
- Software Tools: Familiarity with industry-standard software and programming languages, including Python, TensorFlow, and R, which are integral to machine learning applications.
- Field Trips: Visits to tech companies and research institutions, offering insights into the latest advancements in machine learning and networking with professionals in the field.
- Digital Tools: Access to cloud computing resources and platforms like AWS and Google Cloud, enabling students to work on scalable machine learning projects.
- Libraries and Resources: Extensive access to the University of Maryland libraries, which house a wealth of resources on machine learning and artificial intelligence.

Progression & Future Opportunities

The MS in Applied Machine Learning at the University of Maryland College Park equips graduates with the skills and knowledge to excel in a rapidly growing field. Alumni often find themselves in high-demand roles such as Data Scientist, Machine Learning Engineer, AI Researcher, and Business Intelligence Analyst. With the increasing reliance on data-driven decision-making across industries, the opportunities for career advancement are both abundant and lucrative.

Here’s what you can expect in terms of progression and future opportunities:

- University Services: The university offers robust career services, including personalized career counseling, resume workshops, and networking events with industry professionals. The Maryland Technology Enterprise Institute (Mtech) also provides resources for entrepreneurship and innovation.

- Employment Stats and Salary Figures: According to recent data, graduates from the program have a high employment rate, with over 90% securing jobs within six months of graduation. The average starting salary for graduates in this field is around $100,000, with many positions offering significant growth potential.

- University–Industry Partnerships: The University of Maryland has established partnerships with leading tech companies such as Google, Amazon, and IBM, providing students with internship opportunities and real-world projects that enhance their learning experience.

- Long-term Accreditation Value: The program is accredited by the Middle States Commission on Higher Education, ensuring that the education you receive meets high standards and is recognized by employers globally.

- Graduation Outcomes: Graduates of the MS in Applied Machine Learning program have gone on to work at prestigious organizations, contributing to innovative projects and advancing their careers in various sectors, including healthcare, finance, and technology.

Further Academic Progression: After completing the MS in Applied Machine Learning, you may choose to pursue a Ph.D. in Computer Science or a related field, focusing on advanced research in machine learning and artificial intelligence. This path can lead to academic positions or high-level research roles in industry, allowing you to contribute to cutting-edge developments in technology. Additionally, you could explore certifications in specialized areas such as deep learning or data analytics to further enhance your expertise and career prospects.