芝加哥大学计算机科学硕士+ 查看更多
芝加哥大学
计算机科学硕士
+ 查看更多
- 该项目提供全面的以职业为导向的计算机科学教育,将计算机基础与技术应用相结合
- 该课程适合未来从事程序开发,数据分析,高性能计算和软件工程等职业方向的学生
- 该项目的核心课程包括编程,算法和系统课程。选修课包括软件工程,大数据,数据分析,机器学习,高性能计算,应用程序,云计算,信息安全等课程
- 学生可以根据自己的职业目标和背景制定相应课程,对于没有变成背景的学生,学校将在入学后提供数学,编程等先修课
项目时长:1年全日制项目
地理位置:美国 伊利诺伊州 芝加哥
申请要求
学术 | • 申请人应取得美国认可的学士学位 学术背景: • Programming: • Data types (native and derived) • Operators, precedence, and expressions • Assignment and statements • Control flow (conditionals and iteration) • Functions, return types, and parameters • Recursion • Console and file I/O Math: • Logic: propositional logic; quantifiers • Mathematical reasoning: methods of proof, direct proof, and indirect proof. Mathematical induction and strong induction • Counting: methods of counting; permutations, combinations, binomial theorem, pigeonhole principle, inclusion-exclusion • Discrete probability: discrete probability spaces; conditional probability and independence; Bernoulli trials, Bayes’s theorem, random variables, and expected value; variance, geometric and binomial distributions • Asymptotic notation • Recurrences and methods of solving recurrences • Graphs: simple graphs, isomorphism, paths, trees • Modular arithmetic, divisibility, prime numbers; GCD and Euclid’s algorithm, Fermat’s little theorem |
TOEFL | 总分:90+ |
GRE | 必须递交 |
IELTS | 总分:7+ |
其他要求 |
申请流程
第1步:准备好申请文件 |
三封推荐信;企业的或学术的均可
·In your required candidate statement, we will ask for you to detail your career goals and how an MS in CS from UChicago will help you achieve those goals. Be specific as to why the MPCS. This essay is limited to 500 words, and is required for admission to the MPCS. ·In addition to the required candidate statement, we ask a series of optional, short answer questions to better gage your experience level. Each answer is limited to 250 words. In these short answer questions we ask about your background in mathematics and programming, if you have one, any technical work experience or internships you may have had.
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第2步:需要的其他文件 |
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第3步:在线填表申请 |
第4步:递交申请费 |
Introduction to Computer Science
Introduction to Computer Systems
Introduction to Human-Computer Interaction
Introduction to Robotics
Software Construction
Programming Languages
Computer Architecture
Parallel Computing
Surveillance Aesthetics:Provocations About Privacy and Security in the Digital Age
Introduction to Computer Graphics
Mathematical Foundations of Machine Learning
Discrete Mathematics
Introduction to Complexity Theory
Introduction to Cryptography
Picturing Quantum Processes
Networks and Distributed Systems
Data Intensive Computing Systems
The Value of Data
Deep Learning Systems: Computational Aspects of Large Language Models
Geometric Deep Learning
Topics in Optimization
Introduction to Data Science
Artificial Intelligence, Algorithms, and Human Rights
Mathematical Logic I
Creative Machines and Innovative Instrumentation
Mathematical Computation I: Matrix Computation
Machine Learning
Introduction to Statistical Machine Learning
Fundamentals of Deep Learning
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