The machine learning master’s degree program at Stevens consists of five core courses including Artificial Intelligence, deep learning and natural language processing, and an interdisciplinary list of electives.
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Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In applying statistics to, e.g., a scientific, industrial, or social problem, it is conventional to begin with a statistical population or a statistical model process to be studied. Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can teach themselves to grow and change when exposed to new data.
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Machine Learning and Natural Language Processing define the current state of the art of Artificial Intelligence. These technologies, which are a form of data mining and data analysis, continuously learn from the provided information. They recognize hidden patterns that often provide dramatic competitive advantages, at relatively low costs to the organization. These technologies create significant improvements in the way we work, interact, and live – producing efficiencies never imagined before.
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The Signal Processing and Machine Learning (SPML) program is intended for students looking for a jump-start on a career in data science, with a passion for quantitative thinking, practical problem-solving, computer programming, and applications to a variety of domains. It is designed for motivated students ready for the rigors of an accelerated program. Extremely well-prepared students may complete the program within 12 months, but many students will likely find a 16-month time frame more appropriate.
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Machine learning offers a new paradigm of computing- computer systems that can learn to perform tasks by finding patterns in data, rather than by running code specifically written to accomplish the task by a human programmer. The most common machine learning scenario requires a human teacher to annotate data (identify relevant phenomenon that occurs in the data), and use a machine learning algorithm to generalize from these examples. Generalization is at the heart of machine learning-- how can the machine go beyond the provided set of examples and make predictions about new data. In this class we will look into several machine learning paradigms and specific learning algorithms, analyze their performance and learn the theory behind them.
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The machine learning master’s degree program at Stevens consists of five core courses including Artificial Intelligence, deep learning and natural language processing, and an interdisciplinary list of electives. Courses focus on both theoretical analysis and implementation of a wide range of topics in machine learning. Students can optionally work on a thesis with one of the program's faculty members.
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This course covers modern machine learning theory and techniques that can be applied to make informed data-driven decisions. Machine learning algorithms are being used in a wide range of domains including image and voice recognition, finance, security, and games. Electrical and Computer Engineering Sections Electrical and Computer Engineering.