国际学生入学条件
The minimum undergraduate grade point average (GPA) requirement for the MS in computer science program is 3.0 or equivalent score on a 4.0 scale. Applicants should have the equivalent of a four-year undergraduate degree.
Entrance Exam: The Graduate Record Exam (GRE) is required. Submit GRE results not older than five years. Successful applicants have typically had GRE scores of 150 verbal and 160 quantitative or better.
GRE is waived for applicants with a bachelor of science in computer science or computer engineering from a regionally accredited institution in the United States.
English Language Proficiency: Applicants are required to have a command of oral and written English. Those who do not hold a baccalaureate or other advanced degree from the United States, OR a baccalaureate or other advanced degree from a predetermined country on the waiver list, must meet the minimum language proficiency score requirement in order to be considered for admission.
Paper-based TOEFL: 550, Internet-based TOEFL: 80, IELTS: 6.5, PTE: 53, Duolingo: 105
Resume: Submit a detailed resume indicating your work experience and background.
Letters of Recommendation: Two letters from references who can evaluate your work and/or academic achievements are required.
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IDP—雅思考试联合主办方

雅思考试总分
6.5
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雅思考试指南
- 雅思总分:6.5
- 托福网考总分:80
- 托福笔试总分:550
- 其他语言考试:PTE - 53
CRICOS代码:
申请截止日期: 请与IDP顾问联系以获取详细信息。
课程简介
专注于人工智能使学生对用于体现具有人类智能功能的机器的原理和技术有深入的了解。专注于此的学生将有机会学习并在人工智能的不同领域进行动手实验,例如软件代理,多代理和多机器人系统,机器视觉和图像处理技术,基于神经网络的自适应软件系统,用于关键决策的试探法和随机优化技术,用于在计算机和信息系统中嵌入智能的机器学习和知识工程技术。计算机科学系提供144小时的综合本科生课程,包括计算机科学学士学位和计算机科学学位的硕士学位。它允许合格的学生在完成其本科学位的同时,攻读计算机科学的硕士学位。有关此程序的更多信息,请联
The concentration in artificial intelligence provides students with an in-depth understanding of the principles and technologies used to embody machines with human-like intelligent capabilities. Students taking this concentration will have an opportunity to learn, as well as perform hands-on experiments in different areas of artificial intelligence such as software agents, multi-agent and multi-robot systems, machine vision and image processing technologies, neural network based adaptive software systems, heuristics and stochastic optimization techniques for critical decision making, machine learning and knowledge engineering techniques for embedding intelligence in computers and information systems.<br>Students completing this program will be able to:<br>Demonstrate the ability to create basic computational artifacts. Demonstrate knowledge of computing systems and their integration.<br>Explain how computing permeates today's society, including security, privacy, and ethical considerations.<br>Apply appropriate pedagogical content knowledge in the teaching of computing topics.<br>Describe relevant and recent research findings in computer science education including how they might be applied in the classroom.
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