电气和计算机工程哲学博士-机器学习和数据科学
Doctor of Philosophy in Electrical and Computer Engineering - Machine Learning and Data Science

学历文凭
Ph.D.

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
A B.S. and/or M.S. degree in engineering, physical sciences, or mathematics from an accredited college or university
Minimum cumulative GPA of 3.0 (on a 4.0 scale or its equivalent)
TOEFL with a minimum score of 550 (paper based test-PBT) or 85 (internet based test-iBT).
IELTS with a minimum Band Score of 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:Pearson Test of English (PTE) Academic with a minimum overall score of 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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学校排名
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30
数据源:泰晤士高等教育世界大学排名
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