国际学生入学条件
An Honours Bachelor degree in Computer Science or Engineering (or equivalent degree) with at least a 78% standing.
Three references, at least two academic
Proof of English language proficiency, if applicable. TOEFL 100 (writing 26, speaking 26), IELTS 7.5 (writing 7.0, speaking 7.0)
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雅思考试总分
7.5
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雅思考试指南
- 雅思总分:7.5
- 托福网考总分:100
- 托福笔试总分:160
- 其他语言考试:PTE (Academic) - 68 (writing 65, speaking 65)
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
机器学习是与计算机科学交叉的统计专业领域
The David R. Cheriton School of Computer Science has an international reputation in teaching, academics, research, and employment. We attract exceptional students from all over the world to study and conduct research with our award-winning faculty. You can participate in research projects in a wide variety of topics with our internationally acclaimed researchers. Our research spans the field of computer science, from core work on systems, theory and programming languages to human-computer interaction, DNA and quantum computing to theoretical and applied machine learning, just to name a few. As a graduate student, you will: Access research-intensive lab spaces. Gain the opportunity to publish your work in top conferences and journals. Present at premier conferences in front of peers, industry leaders, researchers, and experts in your field. As a graduate student, you will have the independence to pursue your preferred area of research with a faculty supervisor, or complete eight courses to fulfill your degree requirements through the coursework option<br><br>Machine learning is an area of specialization of statistics crossed with computer science, most notably with such areas as computational statistics, scientific computation, data visualization and computational complexity. We live in an era where information technologies allow individuals and large organizations to gather increasingly large volumes of data about business transactions, web click traces, health records, etc. This data contains a wealth of information, however, ''mining'' the data to extract relevant information is challenging. For instance, how can a fraud be identified from a stream of transactions, how can user preferences be inferred from click traces to improve web services, how can new health indices be designed based on logs of physiological measurements to better assess and monitor chronic diseases
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