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Study Artificial Intelligence at Oxford University (Full Online Course)

Country: UK
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Details

Overview

Artificial Intelligence: Cloud and Edge Implementations is a pioneering course (previously called Data Science for Internet of Things) which covers AI, edge computing, product development and engineering.

You will learn about creating next generation data products driven by AI to help you to transition your career to AI.

This course is designed to create a new breed of engineer, through a solid grounding in artificial intelligence (AI) and edge computing (Internet of Things) for developing systems in production. We incorporate an agile data science methodology adapting agile techniques to AI systems deployed in production. We also cover problem framing and solution deployment through automated machine learning  spanning AI and edge.

The course is designed for industry practitioners with a background of coding. Previous students have used the course to start a new career, for career progression or to have their skills upgraded by their employer

The course takes a problem-solving approach and uses specific case studies from industry.

Participants are expected to have a mind-set of exploration and to study and learn beyond the class material itself (depending on their existing familiarity with the subject matter).

The course provides you skills in cloud programming (Azure, AWS and Google) and Python development (TensorFlow and Keras).

Programme details

Python (TensorFlow and Keras) is the primary language of the course and while we do not expect you to have full proficiency in it, we expect you to have a programming background. This is an industry course not an academic course and we focus on skills based/commercial products.

The course explores the following themes:

  • Principles and foundations for artificial intelligence and edge computing
  • Python coding (TensorFlow and Keras)
  • Core deep learning algorithms (MLP, CNN, LSTMs)
  • Reinforcement learning
  • Unsupervised learning, generative adversarial networks (GANs) and autoencoders
  • End-to-end agile problem-solving methodology including continuous improvement and delivery for AI models in production
  • Robotics (Dobot)
  • Cloud based AI implementations – Azure , Google and Amazon
  • Autonomous vehicles applications
  • Time series
  • Programming sprints
  • Industry use cases and examples: retail, oil and gas, autonomous vehicles, affective computing (emotional AI)
  • Natural language processing
  • Industrial IoT (anomaly detection and failure prediction)
  • Industry 4.0
  • Devops (in relation to AI application deployment)
  • Architecture for AI applications in production

Term One

  • Principles and foundations for artificial intelligence and edge computing
  • Python, TensorFlow and Keras for data science
  • Programming sprints
  • Unsupervised learning including autoencoders and variational autoencoders
  • Natural language rocessing
  • Agile methodology for data science
  • Retail case studies
  • Edge computing with Intel
  • AI – cloud and the edge (Azure)
  • Industry use cases (Ocado)
  • AI – cloud and edge (AWS)

Term Two

  • Reinforcement learning
  • Robotics sprints (cloud robotics i.e. integrating cloud APIs into the Dobot robotic arm)
  • AI – cloud with Google
  • Agile methodology for AI production systems (two sessions)
  • End-to-end deep learning frameworks
  • Affective computing (emotional AI)
  • Autonomous vehicles
  • Industrial IoT – Industrial IoT (anomaly detection and failure prediction)

Note: the programme details and tutors named on this course may be subject to minor changes.

Specifications

Type of Opportunity Scholarships and Fellowships
DeadlineOngoing
CountryUK
Open toAll Nationalities
OrganizerOxford University

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