电气和计算机工程理学硕士-机器学习和数据科学
Master of Science in Electrical and Computer Engineering - Machine Learning and Data Science

学历文凭
Masters Degree

专业院系
数据库管理

开学时间

课程时长

课程学费

国际学生入学条件
Applicants must hold a bachelor's degree or the equivalent from an accredited institution in the United States or from a recognized university-level academic institution abroad. At least a B average (3.0 GPA) or its equivalent is required for admission.
TOEFL score required for admission is 550 for the paper-and-pencil version, 213 for the computer-based test or 85 for the internet-based test (iBT)
IELTS score required for admission is Band Score 7
IDP—雅思考试联合主办方

雅思考试总分
7.0
了解更多
- 雅思总分:7
- 托福网考总分:85
- 托福笔试总分:550
- 其他语言考试:Minimum PTE Academic score required for graduate admission is Overall Score- 65.
CRICOS代码:
申请截止日期:请 与IDP联系 以获取详细信息。
课程简介
电气和计算机工程系(ECE)允许学生通过MS或Ph.D研究生课程。程式。ECE提供研究生课程,可攻读MS和Ph.D。电气工程学士学位,具有以下各个领域的专业:通信理论与系统,计算机工程,电子电路与系统,电子设备与材料,智能系统,机器人与控制,磁记录,光子学,无线电与空间科学以及信号和图像处理。此外,在先进制造,应用海洋科学和材料科学领域设有跨学科课程。MS程序也以研究为导向,旨在为随后的博士学位提供必要的强化技术准备。提供了两种学习计划,都需要成功完成48个季度单元的课程学分。计划I是课程工作和研究的结合
Data has become central to our daily lives and there is growing demand for professionals with data analysis skills. Computing and data play an ever-growing role in all areas of human knowledge. Applications of Machine Learning and Data Science are now pervasive in a wide variety of businesses looking to use data effectively, as well as in government agencies, academia and health care. These applications have led to an increased need to fundamentally understand the underlying mechanisms of statistical learning on these datasets, develop new and more powerful software and hardware tools for maximizing information extraction, and new strategies for application of these processes across a wide variety of application domains.<br><br>Our faculty are developing across the spectrum of deep theoretical and algorithmic foundations for data analytics and machine learning, and catered applications of these techniques to solve important human, institutional, and societal challenges.
Data has become central to our daily lives and there is growing demand for professionals with data analysis skills. Computing and data play an ever-growing role in all areas of human knowledge. Applications of Machine Learning and Data Science are now pervasive in a wide variety of businesses looking to use data effectively, as well as in government agencies, academia and health care. These applications have led to an increased need to fundamentally understand the underlying mechanisms of statistical learning on these datasets, develop new and more powerful software and hardware tools for maximizing information extraction, and new strategies for application of these processes across a wide variety of application domains.<br><br>Our faculty are developing across the spectrum of deep theoretical and algorithmic foundations for data analytics and machine learning, and catered applications of these techniques to solve important human, institutional, and societal challenges.
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30
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