Master of Science in Signal Processing and Machine Learning-威斯康星大学麦迪逊分校申请条件

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Master of Science in Signal Processing and Machine Learning

威斯康星大学麦迪逊分校

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学历文凭
Masters Degree
专业院系
人工智能与机器人
开学时间
课程时长
课程学费
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国际学生入学条件

A bachelor's degree from a regionally accredited U.S. institution or a comparable degree from an international institution is required. International applicants must have a degree comparable to a regionally accredited U.S. bachelor's degree. You must have completed your undergraduate degree, or similar, before starting graduate school.

A minimum undergraduate grade-point average (GPA) of 3.00 on the equivalent of the last 60 semester hours (approximately two years of work) or a master's degree with a minimum cumulative GPA of 3.00 is required. Applicants from an international institution must demonstrate strong academic achievement comparable to a 3.00 for an undergraduate or master's degree. The Graduate School will use your institution's grading scale. Do not convert your grades to a 4.00 scale. Minimum IELTS -7.0 , Minimum TOEFL IBT - 92 ,TOEFL PBT - 580.

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雅思考试总分

7.0

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雅思考试指南

雅思总分：7
托福网考总分：92
托福笔试总分：580
其他语言考试：NA

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课程简介

The Signal Processing and Machine Learning MS program educates students in the foundations of data science theory and methods. Graduates of this program will be poised to immediately participate in data analysis tasks in a variety of application domains using tools based in linear algebra, statistics, and optimization. The coursework includes a summer practicum that gives students hands-on experience with real-world datasets. 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 students ready for the rigors of a 12-16 month accelerated program. Extremely well-prepared students can complete the program in 12 months. However, many students will find a 16-month timeframe best.

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