模板留学服务-CS
美国-杜克

杜克大学
计算机科学硕士


简 介

  • 计算机科学系提供了算法、架构、人工智能、科学计算和系统等领域的硕士和博士学位课程

  • 该项目包括了30个学分的授课和一个论文项目或者一个实战项目。学生可以根据未来规划自行选择


项目时长:1.5—2年全日制项目

项目授课地点:美国 北卡罗莱纳州 达勒姆
申请要求


学术
 
• 申请人应取得美国认可的学士学位,并且拥有强大的计算机科学,数学或相关背景


TOEFL


总分:90+


GRE


需要递交

IELTS


总分:7+
其他要求



申请流程

第1步:准备好申请文件
  • 推荐信
三封推荐信;如果是在校生至少一封来自教授;如果已经就业,三封企业推荐信也是可以的

  • Personal Statement:
Submit SOP- 
·The document should be one page, single-spaced, in at least a 10 point font with 1-inch margins
·Statement may spill onto a second page, but it shouldn’t be two full pages or more
     
Applicants need to answer- "Why do you want to study engineering at Duke?"
·Think about it in two ways: why is this program the right one for you, and why are you right for this program? How will Duke help you meet your future goals?

  • CV
Applicants must submit a Resume
It should highlight the applicant's experience, activities, and leadership, whether in the classroom, in a volunteer setting, a  club  or organization, or on the job
第2步:需要的其他文件
  • 每所就读大学的学校颁发的成绩单
  • 官方学位证书(申请时如未毕业可不提供)
  • 提交英语语言能力成绩
  • 护照复印件
  • 递交GRE成绩
  • 本科和/或研究生学位的记录 
第3步:在线填表申请
4步:递交申请费


GT备考
Artificial Intelligence/Machine Learning Concentration:
  • Breadth Requirement: CompSci 570 (Artificial Intelligence) or CompSci 671. If you take CompSci 570, then you can count CompSci 671 towards the depth requirement.
  • Depth Requirement: In addition to the one breadth course, at least two courses from the following list. Not all of these courses will be consistently offered, so please plan accordingly.
   o   CompSci 671 (Theory and Algorithms for ML); If you did not take 570, then you need to count 671 towards the breadth and not the depth requirement.
   o   CompSci 590.XX (Introduction to Natural Language Processing)
   o   CompSci 590.XX (Machine Learning Algorithms)
   o   CompSci 590.XX (Reinforcement Learning)
   o   CompSci 527 (Computer Vision)
   o   CompSci 571 (Probabilistic ML)
  • Elective Requirement: At least one course in AI/ML-adjacent areas, from either inside or outside the department. These include, but are not restricted to the following. Please consult the DGS to check if a course you plan to take can count as an elective. You can take an extra depth course to satisfy this requirement as well.
   o   CompSci 555 (Probability and Statistics for ECE)
   o   CompSci 590 (Topics in Computational Biology)
   o   CompSci 561 (Computational Sequence Biology)
   o   CompSci 590 (Cryo-EM Analysis)
   o   CompSci 590 (Computational Economics)
   o   CompSci 675 (Introduction to Deep Learning)
   o   ECE 590 (Machine Learning in Adversarial Settings)
   o   ECE 590 (Advanced Topics in Deep Learning)
   o   ECE 661 (Comp. Eng. Machine Learning and Deep Neural Networks)

Cybersecurity Concentration:
  • Breadth Requirement: CompSci 5XX (Graduate Computer Security). You can also take CompSci 510 or CompSci 514 to satisfy this requirement.
  • Depth Requirement: In addition to the breadth course, at least two courses from the following list. Not all of these courses will be consistently offered, so please plan accordingly.
   o   CompSci 5XX: Graduate Computer Security.  If you count this course towards the breadth requirement, you cannot count it towards the depth requirement. If you count this as a depth course, you need to take two other depth courses instead of one other depth and one elective course.
   o   CompSci 590 (Secure Software Systems)
   o   CompSci 590 (CryptoCurrency)
   o   CompSci 590 (Applied Cryptography)
   o   CompSci 590 (Blockchain)
   o   CompSci 590 (Privacy and Fairness)
   o   CompSci 590 (Cloud Security)
   o   CompSci 590 (Cryptography)
  • Elective Requirement: Any one course in security-adjacent areas, from either inside or outside the department. These include, but are not restricted to the following. Please consult the DGS to check if a course you plan to take can count as an elective. You can take an extra depth course to satisfy this requirement as well.
   o  CompSci 516 (Database Systems)
   o   CompSci 555 (Probability and Statistics for ECE)
   o   CompSci 554 (Fault Tolerant and Testable Computer Systems)
   o   CompSci 590 (Comp. Arch and Hardware Acceleration)
   o   CompSci 590 (Edge Computing)
   o   CompSci 590 (Coding Theory)
   o   CompSci 630 (Randomized Algorithms)
   o   CompSci 650 (Advanced Computer Architecture)
   o   ECE 590 (Machine Learning in Adversarial Settings)

Portfolio Requirement: In addition to the course requirements, the student will have to submit a final portfolio outlining what they have done in the concentration area that goes beyond merely taking courses. This could include internships, attending seminars or reading groups, mentoring undergraduate students in CS+, TA-ing courses, and so on. These activities should be associated with the planned concentration area, and this will be checked as part of the exit interview. Please list at least two such activities in the portfolio.

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