帝国理工学院 计算机理学硕士

帝国理工学院

计算机理学硕士

（机器学习）

The MSc in Computing (Specialism) is a taught postgraduate course aimed at students with a good academic degree who may not have studied computing exclusively but who have studied a considerable amount of computing already.

计算机理学（具体研究方向）硕士项目针对可能没有专门学习过计算机专业知识，但已经掌握大量计算机知识的具有优秀学位的学生。

Applicants who want to become a specialist in a particular area of computing and who want to take a first crucial training step towards that goal are the natural target for this course. The expectation of graduates is that this course enables them to have rewarding careers as specialist in an area of Computing.

课程适合想在计算机某特定领域接受训练并成为专家的申请者。

Each specialism has a flexible mix of breadth and depth, consisting of two or three compulsory modules as well as choices from a selection of core and optional modules.

每个具体研究方向的课程，广度和深度不一，包括两或三个必修模块以及一系列核心和选修模块。

This course is also a suitable preparation for PhD studies.

本课程也能为博士研究做准备。

Imperial College London

MSc in Computing (Machine Learning)

计算机理学（机器学习）硕士项目是计算机理学（具体研究方向）硕士项目之二

This course specialises in sophisticated data mining and machine learning techniques, exploiting scalable data management and processing infrastructures, including neurotechnology, bioinformatics, security and human-centered computing.

本课程专注于复杂的数据挖掘和机器学习技术，利用可扩展数据管理和处理基础设施，包括神经技术，生物信息学，安全和以人为中心的计算。

课程结构

Compulsory 必修课

AUTUMN 秋季

496 Mathematics for Machine Learning

面向计算机学习的数学

534 Short Introduction to Prolog

Prolog概论

SUMMER 夏季

542 MSc Computing Science (Specialist) Individual Project

计算机科学（机器学习）个人项目

Selective: Choose the equivalent of between five and eight full courses from the list below. Courses whose number ends in H are half courses.

限选课：从下面选择五到八门课程，注意以H结尾的课程是半学年课程

AUTUMN 秋季

316 Computer Vision 计算机视觉

333 Robotics 机器人技术

424H Reinforcement Learning(Half Course)

强化学习（H）

474 Machine Arguing

机器辩论

477 Computational Optimisation计算优化

499H Modal Logic(Half Course)

模态逻辑（H）

AUTUMN AND SPRING 秋季和春季

531 Prolog

SPRING 春季

304 Logic-Based Learning基于逻辑的学习

395 Introduction to Machine Learning

机器学习导论

416 Machine Learning for Imaging

用于成像的机器学习

433 Advanced Robotics 高级机器人技术

460 Deep Learning 深度学习

493 Probabilistic Inference

概率推理

498H Logics for Strategic Reasoning in AI(Half Course)

人工智能中的战略推理逻辑（H）

Optional: Choose the equivalent of between zero and three full courses from the list below. Courses whose number ends in H are half courses.

选修课：从下面选择零到三门课程，注意以H结尾的课程是半学年课程

AUTUMN 秋季

337 Simulation and Modelling 仿真与建模

343 Operations Research 运筹学

349 Information and Coding Theory 信息和编码理论

382 Type Systems for Programming Languages

程序设计语言的类型系统

408 Privacy Engineering隐私工程

410 Scalable Distributed Systems Design

可缩放分布式系统设计

438 Complexity 复杂性

445H Advanced Security(Half Course)

高级安全（H）

471 Advanced Issues in Object Oriented Programming

面向对象编程的前沿问题

474 Machine Arguing 机器辩论

484 Quantum Computing 量子计算

572 Advanced Databases 高级数据库

SPRING 春季

317 Graphics 制图学

318 Custom Computing 定制计算

331 Network and Web Security 网络和网站安全

332 Advanced Computer Architecture

高级计算机体系结构

338 Pervasive Computing 普适计算

339 Performance Engineering 性能工程

347 Distributed Algorithms 分布式算法

409 Cryptography Engineering 加密工程

417 Advanced Computer Graphics 高级计算机制图学

422 Computational Finance 计算金融

446H Applied Network Security(Half Course)

应用网络安全（H）

467 Principles of Decentralized Ledgers

分布式账本原则

469 Probabilistic Model Checking and Analysis

概率模型检测与分析

512 Independent Study Option 自主学习

项目申请要求

Minimum academic requirement 最低学术要求

Our minimum requirement is at least a2.1 UK Honour's degree.

最低要求是二等甲级学位

While there is no minimum requirement for GRE scores, a strong application would include scores higher than 159 for Quantitative Reasoning and higher than 145 for Verbal Reasoning.

GRE：数学>159；语文>145

English language requirement 英语要求

standard College requirement：6.5 overall (minimum 6.0 in all elements)

雅思成绩：6.5（单科至少6.0）