帝国理工学院应用机器学习专业项目网站

Applied Machine Learning

英国公立

项目背景

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更新时间:2024-08-23

专业排名14

大学帝国理工学院

开学时间10月

课程学制1年

学费41750.00/GBP

专业介绍

During this course, you will focus on applying machine learning to electrical engineering. Applications include robotics, computer vision bio-inspired learning, communication and signal processing. This course is perfect if you’re interested in developing real-world systems. These will involve signals, sensors and hardware, such as robots or communication devices. Address the challenge of how automated systems can learn from signals and data as they operate in real environments. Learn through specialised modules including a specialised computer laboratory module. These allow you to develop practical skills with industrial input and your own individual project. Learn the theoretical basis for machine learning systems, design methods and algorithms for modelling real systems Equip yourself to a range of careers. These could involve the design, modelling, analysis and control of intelligent signal and data processing Develop advanced knowledge of machine and deep learning in engineering Gain skills that enable you to continue your studies to PhD level Through the course, you will learn from field-leading staff. You will gain a critical awareness of current issues, research and their applications. Applying different methodologies, you will tackle complex issues systematically and creatively.

推荐顾问

世界排名

2020年

14

2019年

15

2018年

12

2017年

12

语言要求

类型
雅思
托福
PTE
Listen
6.5
22
62
Speak
6.5
22
62
Read
6.5
22
62
Write
6.5
22
62
总分
7
100
69

学术要求

领域:电子电气工程或相关含substantial电子,电气科目的专业;如学术条件不足可以尝试用3年工作经验冲刺。 英本:75%

课程设置

Machine Learning:机器学习 Lab in AML: AML实验室 Deep Learning: 深度学习 Artificial Intelligence: 人工智能 Computer Vision and Pattern Recognition: 计算机视觉和模式识别 Digital Image Processing: 数字图像处理 Human Centered Robotics: 以人为中心的机器人 Probability and Stochastic Processes: 概率和随机过程 Speech Processing: 语音处理 Self Organising Multi Agent Systems: 自组织多智能体系统 Topics in Control Systems: 控制系统中的主题 Topics in Large Dimensional Data Processing: 大维数据处理中的主题 Wavelets Representation Learning and their Applications: 小波表示学习及其应用

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