Computer Vision

Overview

Computer Vision is a brand new, three-day course which is designed for practitioners already applying or considering computer vision techniques. This is an industry-led course rather than a purely academic course.

Programme details

Computer vision is one of the fastest growing areas in artificial intelligence.

Computer vision applications aim to develop systems that have similar capabilities as human vision / cognition by understanding digital images. The capacity to perceive the complex visual world through images enables computers to provide solutions that automate existing processes.

The development of new deep learning techniques enable computers to provide solutions which are significantly better than human visual abilities. The availability of algorithms, data and computing power empower us to create new applications in computer vision.

1. Computer vision concepts and fundamentals

Introduction to machine learning and deep learning, training and deploying machine learning and deep learning models, fundamentals of neural networks including:

  • The perceptron algorithm
  • Backpropagation
  • Multi-layer perceptrons
  • Gradient descent
  • Optimisation

Fundamentals of convolutional neural networks (CNNs)

  • Understanding convolutions
  • Building blocks of CNNs
  • Implementing a basic CNN

Fundamentals of Image Processing and Analysis

  • image classification
  • detection
  • reconstruction
  • segmentation
  • style transfer

Examples of Datasets for training Image Analysis models

  • CIFAR-10
  • ImageNet

Libraries and Packages

Libraries and packages used for deep learning and computer vision using Python including Keras, TensorFlow 2.0, Mxnet, OpenCV, scikit-image, etc.

CNN architectures

An understanding of CNNs and their evolution including AlexNet, GoogLeNet, VGGNet, ResNet, etc.

2. Computer vision applications and scenarios

In this section, we provide an overview of the scenarios and applications that we will use in the course. Some of these scenarios will be implemented in code over the duration of the course, including:

  • object detections
  • cars and pedestrian detection
  • use in drones
  • anomaly / novelty detection,
  • robotics,
  • facial detection,
  • healthcare (diagnosis using X-rays, MRIs, tumours, etc.),
  • security,
  • working with large images (e.g. from space, or landscape images from drones),
  • manufacturing (object counting, detecting damaged components, etc).

Hands-on coding exercises

Some of the scenarios listed above will be implemented over the duration of the course as hands-on coding exercises.

Creating and improving an image classifier

We develop a basic image classifier and continue to improve it over the course.

Computer vision on edge devices

Covering inference at the edge using devices like Nvidia Jetson Nano, Raspberry Pi, etc.

Computer vision using the cloud

Covering issues like using pre-built models in the cloud to complement your own images.

This is a hands-on course implementing computer vision applications, providing you with:

  • An understanding of computer vision applications
  • An understanding of computer vision applications on edge devices
  • Computer vision applications in the cloud

Certification

Participants who attend the full course will receive a University of Oxford certificate of attendance. This will be presented to you prior to the end of the course wherever possible.

The certificate will show your name, the course title and the dates of the course you attended. 

Fees

Description Costs
Course fee £1295.00

Payment

Fees include course materials, tuition, refreshments and lunches. The price does not include accommodation.

All courses are VAT exempt.

Register immediately online 

Click the “book now” button on this webpage. Payment by credit or debit card is required.

Request an invoice

If you require an invoice for your company or personal records, please complete an online application form. The Course Administrator will then email you an invoice. Payment is accepted online, by credit/debit card, or by bank transfer. Please do not send card or bank details via email.

Tutors

Dr Lars Kunze

Course Director

Lars Kunze is a Departmental Lecturer in Mobile Robotics with the Oxford Robotics Institute, within the Department of Engineering Science, University of Oxford, Oxford, U.K., and a Stipendiary Lecturer of Computer Science with Keble College, Oxford, U.K.

His research concerns the design and development of fundamental AI techniques for autonomous robot systems that operate in complex, real-world environments. In particular, he focuses on semantic scene understanding using logical as well as statistical-relational models. Within the Oxford Robotics Institute, he works on transparent and interpretable models that can provide detailed explanations in the context of autonomous driving.

Lars studied Cognitive Science (BSc, 2006) and Computer Science (MSc, 2008) at the University of Osnabrück, Germany, and partly at the University of Edinburgh, UK. He received his PhD (Dr. rer. nat.) from the Technical University of Munich, Germany, in 2014.

During his PhD, Lars worked on methods for naive physics and common sense reasoning in the context of everyday robot manipulation. He contributed to several national, European and international projects including RoboHow, RoboEarth, and the PR2 Beta program.

In May 2013, Lars was appointed as a Research Fellow in the Intelligent Robotics Lab at the School of Computer Science at Birmingham University.  Here he worked on qualitative spatio-temporal models for perception planning and knowledge-enabled perception, contributing to the European research projects STRANDS and ALOOF.

He was a visiting researcher in the JSK Lab at the University of Tokyo, Japan (Summer 2011) and the Human-Robot Interaction Laboratory at Tufts University, US (Spring 2015).

Ajit Jaokar

Course Director

Based in London, Ajit's work work spans research, entrepreneurship and academia relating to artificial intelligence (AI) and the internet of things (IoT). 

He is the course director of the course: Artificial Intelligence: Cloud and Edge Implementations. Besides this, he also conducts the University of Oxford courses: AI for Cybersecurity and Computer Vision.

Ajit works as a Data Scientist through his company feynlabs - focusing on building innovative early stage AI prototypes for domains such as cybersecurity, robotics and healthcare.

Besides the University of Oxford, Ajit has also conducted AI courses in the London School of Economics (LSE), Universidad Politécnica de Madrid (UPM) and as part of the The Future Society at the Harvard Kennedy School of Government.

He is also currently working on a book to teach AI using mathematical foundations at high school level. 

Ajit was listed in the top 30 influencers for IoT for 2017 along with Amazon, Bosch, Cisco, Forrester and Gartner by the German insurance company Munich Re.

Ajit publishes extensively on KDnuggets and Data Science Central.

He was recently included in top 16 influencers (Data Science Central), Top 100 blogs (KDnuggets), Top 50 (IoT central), and 19th among the top 50 twitter IoT influencers (IoT Institute). 

His PhD research is based on AI and Affective Computing (how AI interprets emotion).

Bojan Komazec

Tutor

Bojan Komazec has been working in IT industry for over 15 years. He currently holds the position of Senior Software Engineer in Avast Software where he develops various security and privacy products.

Bojan's interests span from code craftsmanship and cyber security to Artificial Intelligence and Internet of Things. He is an active blogger and speaker at several IT Meetup groups where he enjoys sharing experience and knowledge.

Bojan studied Electrical Engineering and Telecommunications and in 2004 received master's degree from University of Novi Sad, Serbia.

Ms Ayşe Mutlu

Tutor

Ayşe Mutlu is a data scientist working on Azure AI and devops technologies. Based in London, Ayşe’s work involves building and deploying Machine Learning and Deep Learning models using the Microsoft Azure framework (Azure DevOps and Azure Pipelines).

She enjoys coding in Python and contributing to Open Source Initiatives in Python.

Application

If you would like to discuss your application or any part of the application process before applying, please click Contact Us at the top of this page.

Accommodation

Although not included in the course fee, accommodation may be available at our on-site Rewley House Residential Centre. All bedrooms are en suite and decorated to a high standard, and come with tea- and coffee-making facilities, free Wi-Fi access and Freeview TV. Guests can take advantage of the excellent dining facilities and common room bar, where they may relax and network with others on the programme.

To check prices, availability and to book rooms please visit the Rewley House Residential Centre website.