国际学生入学条件
The IEOR PhD program is designed for students with an interest in pursuing advanced studies in Industrial Engineering and Operations Research. Prospective applicants come from a diverse set of backgrounds including Math, Applied Math, Statistics, Computer Science, Electrical Engineering and Operations Research.
Students entering the doctoral program must demonstrate proficiency in the following areas: Real Analysis, Linear Algebra, Probability and Statistics.
The basic requirement for admission as a graduate student is the bachelor's degree received from an institution of acceptable standing. Ordinarily, the applicant will have majored in the field in which graduate study is intended, but in certain programs preparation in a related field of engineering or science is acceptable. The applicant will be admitted only if the undergraduate record shows promise of productive and effective graduate work. The Master of Science (MS) degree is required for admission into the PhD and EngScD degree programs. A student who holds an appropriate bachelor's degree in engineering may apply for admission to either the MS only or MS leading to PhD program.
International applicants or applicants whose undergraduate degree was received in a country in which English is not the official and widely spoken language must submit the following additional requirements with the application for admission:
1. Test of English as a Foreign Language (TOEFL), International English Language Testing System (IELTS) or Pearson Test of English (PTE Academic)
2. Translation of the official transcript(s) and degree/diploma certificate(s) if the institution(s) attended does not issue transcripts in English. The translation must be conducted by a reputable service provider.
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雅思考试总分
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雅思考试指南
- 雅思总分:6
- 托福网考总分:60
- 托福笔试总分:160
- 其他语言考试:NA
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申请截止日期: 请与IDP顾问联系以获取详细信息。
课程简介
Machine learning and artificial intelligence are shaping the current and future practices in business management and decision making, thanks to the vast amount of available data, increase in computational power, and new optimization algorithms. The research at IEOR is at the forefront of this revolution, spanning a wide variety of topics within theoretical and applied machine learning, including learning from interactive data (e.g., multi-armed bandits and reinforcement learning), online learning, and topics related to interpretability and fairness of ML and AI. We are creating machine learning theory, algorithms, and systems for a broad spectrum of application areas, including financial technology, energy, recommendation systems, online advertising, business analytics, service systems, pricing and revenue management. Our faculty and students regularly collaborate with cutting-edge AI technology companies as well as local businesses, e-commerce companies, media houses, government, and financial firms. We work closely with colleagues in computer science and other engineering departments, and play an active role in the Data Science Institute.
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