The Machine Learning and Artificial Intelligence MS Program at NYU
Machine learning is a broad field that can be applied to a variety of fields. For example, it can be used in recommender engines that suggest products to you or speech recognition software that automatically converts voice memos into text. It can also be used in fraud detection services for banks.
Machine learning is one of the most exciting areas in computer science. It allows computers to find patterns and learn new skills without being explicitly programmed by humans. These algorithms are used in a variety of applications, including chatbots, spam filtering, and search engines. They are also used to identify patterns in data and make predictions about future behavior.
This course covers the foundational concepts of machine learning. It provides an introduction to supervised and unsupervised learning, as well as the basics of feature construction, model evaluation, and classification. It also explores some specialized techniques, such as regularization and regularized regression.
This is a challenging course that requires a solid understanding of calculus and linear algebra. It is recommended for students who want to gain a deeper understanding of the field and apply their knowledge to real-world problems. It’s also a good way to see if machine learning is right for you. Students should have some programming experience and a willingness to work hard.
The application process for the Machine Learning and Artificial Intelligence MS program is rigorous. Candidates need to submit a detailed curriculum vitae and strong letters of recommendation. The application review committee places special weight on the letters of recommendation from professors or employers.
All graduate programs at the Tandon School of Engineering are considered STEM programs, which means that students who complete these programs may be eligible for a 24-month extension of STEM OPT after graduation. This is a great benefit to students who want to work in the field of machine learning and artificial intelligence.
Generative AI is an emerging technology that can generate new data sets with different characteristics based on existing datasets. It is used for a variety of purposes, including improving medical diagnoses and personalization of online shopping experiences. NYU Langone’s generative AI private environment is available to authorized members of the university community for innovation/research, mentored exploration projects, and exploring use cases.
The program’s faculty are world-renowned specialists in their disciplines. They have a strong reputation for attracting graduates who work for leading technological businesses or go on to pursue research in academia. The curriculum emphasizes machine learning techniques and systems, as well as mathematics and logic.
The CILVR lab (Computational Intelligence, Learning, Vision, Robotics) brings together researchers with a variety of backgrounds to work on applications of artificial intelligence and machine learning in fields such as computer perception, natural language processing, robotics, and healthcare. Researchers also apply mathematical and theoretical tools to a wide range of other topics in computer science, including systems, optimization, geometry, and computational biology.
Applicants are expected to have basic proficiency in linear algebra and probability theory, and knowledge of some calculus. Applicants should also have programming experience in Java or Python. They will be required to complete 3 to 4 assignments and a project during the semester. The final grade will be based on the average of the assignments and project grades.
Despite its seemingly all-encompassing name, machine learning isn’t synonymous with artificial intelligence (AI). The two terms are often used interchangeably, but AI and machine learning have distinct meanings. While AI is the more general attempt to create machines that exhibit human-like cognitive abilities, machine learning is a specific set of algorithms and data sets that allow computers to autonomously learn from the information they are exposed to. For example, a recommendation engine that suggests products based on previous purchases, a speech recognition application that converts voice memos into text, or a bank’s fraud detection system are all examples of machine learning in action.
NYU Courant Institute of Mathematical Sciences, Department of Computer Science and Center for Data Science are seeking a limited number of non-tenure track faculty positions in the areas of applied mathematics (math for data science/machine learning), physical modeling and scientific computing. Successful candidates will have an outstanding record of scholarship and a strong publication track record.