诺丁汉大学/英国

本科 · 2027

计算机科学与人工智能理学学士荣誉学士

Computer Science with Artificial Intelligence BSc Hons

理学学士Hons · 理学院 · G4G7BSc Hons · Faculty of Science

学制

全日制 3 年3 years full-time

授课

全日制Full-time

学费

英镑 33,500 · UK/home students: £10,050 每年; International students: £33,500 每年GBP 33,500 · UK/home students: £10,050 per year; International students: £33,500 per year

英语

雅思 6.5 · 6.5 (不低于 6.0 in any element)IELTS 6.5 · 6.5 (no less than 6.0 in any element)

入学要求

A levels A*AA; IB 36 points 总分A levels A*AA; IB 36 points overall

学年课程

课程模块Modules

  • 年一Year one
  • 年二Year two
  • Year three

10 modules

  • 逻辑logic
  • sets
  • functions and relations
  • graphs
  • induction
  • 基础概率basic probability
  • statistics and matrices

8 modules

  • sorting algorithms
  • heaps
  • binary search trees
  • hashmaps
  • graph algorithms
  • finite state machines
  • regular expressions
  • context-free grammars
  • 图灵机器Turing machines
  • Lambda calculus
  • 编程与效应programming with effects
  • reasoning about programs
  • 控制流动control flow
  • advanced libraries
  • 改进效率improving efficiency
  • type systems
  • functional pearls
  • overview of parallel and distributed computing
  • applications of distributed systems
  • reliable network communication
  • introduction to distributed data management
  • introduction to distributed algorithms
  • 影像处理与应用image processing and its applications
  • fundamentals of digital images
  • 数字影像处理理论与实践digital image processing theory and practice
  • 应用影像处理applications of image processing

24 modules

  • task environments
  • reactive, deliberative and hybrid architectures for individual agents
  • architectures and coordination mechanisms for multi-agent systems
  • 机器学习基础machine learning foundations
  • pattern recognition foundations
  • artificial neural networks
  • 深学习deep learning
  • 应用机器学习applications of machine learning
  • 数据采矿技术data mining techniques
  • evaluating hypotheses
  • 基础统计学basic statistics
  • types of data
  • 数据可视化技术data visualisation techniques
  • 数据建模data modelling
  • data pre-processing methods including data imputation
  • forecasting methods
  • clustering and classification methods (decision trees, naīve bayes classifiers, k-nearest neighbours)
  • 数据仿真data simulation
  • model interpretation techniques to aid decision support
  • basic lambda calculus
  • operational semantics
  • domain theory
  • types, propositions as types and formal verification

申请节点

September 2027

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