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PLEASE NOTE The Bioinformatics Team are presently teaching as many courses live online, with tutors available to help you work through the course material on a personal copy of the course environment. We aim to simulate the classroom experience as closely as possible, with opportunities for one-to-one discussion with tutors and a focus on interactivity throughout.

This course aims to give you an introduction to the basics of Matlab. During the two day course we will use a practical based approach to give you the confidence to start using Matlab in your own work. In particular we will show you how to write your own scripts and functions and how to use pre-written functions. We will also explore the many ways in which help is available to Matlab users. In addition we will cover basic computer programming in Matlab to enable you to write more efficient scripts.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.

A two-day course designed to introduce MATLAB and some basic programming concepts. Further information is available here.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.

A recommended LinkedIn Learning course, provided by the University of Cambridge's subscription to LinkedIn Learning.

The course can be accessed here.

A recommended LinkedIn Learning course, provided by the University of Cambridge's subscription to LinkedIn Learning.

The course can be accessed here.

An online workshop on how to develop your first MATLAB code.

In this session, you will be introduced to the MATLAB environment and programming language. We will discuss the basic operations and core concepts that form the building blocks of any scientific programme.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Learn the theory and practice of building deep neural networks with real-life image and sequence data.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Create custom visualizations and automate your data analysis tasks.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Learn MATLAB for financial data analysis and modeling.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the Univeristy of Cambridge.

This course provides a comprehensive introduction to common features and workflows in MATLAB.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Use matrix methods to solve systems of linear equations and perform eigenvalue decomposition.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Get started quickly with basic descriptive statistics and data fitting.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Get started quickly with an introduction to symbolic math.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Explore data and build predictive models.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the Univeristy of Cambridge.

Get started quickly using deep learning methods to perform image recognition.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the Univeristy of Cambridge.

Learn the basics of practical machine learning methods for classification problems.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the Univeristy of Cambridge.

This course is an introductory tutorial on commonly used features and workflows.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the Univeristy of Cambridge.

Learn the basics of creating, editing, and simulating state machines in Stateflow.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Improve the robustness, flexibility, and efficiency of your MATLAB code.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

MathWorks are running a series of regular live webinars, tailored to Professors, PhD students & Post-Docs.

Each Wednesday you will discover a technical topic where you can learn about the latest MATLAB capabilities for your research applications.

To register for the sessions click here

These technical sessions will be followed up on Tuesdays with a session covering online teaching, including ready-to-use resources. You will explore how to use MATLAB to increase student engagement in your course.

his is part of a suite of MathWorks online courses available to the Univeristy of Cambridge.

This course is part of a suite of MathWorks online courses available to the Univeristy of Cambridge.

This is an introductory tutorial on commonly used features and workflows.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Use root finding methods to solve nonlinear equations.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

This course is part of a suite of MathWorks online courses available to the University of Cambridge.

Use MATLAB ODE solvers to numerically solve ordinary differential equations.

If you do not already have an account, you will be asked to create one for the MathWorks Academy to access these courses, please use your @cam.ac.uk email address.

This course can be accessed here.

Join us for a two-hour workshop on image processing and analysis in MATLAB. This practical session provides a series of example workflows to extract quantitative data from image files.

This workshop is the first event in Imaging ONE WORLD, a series of events bringing together scientists working from home to deliver workshops and talks on imaging theory and analysis. An initiative made up of scientists, imaging systems and software providers in collaboration to deliver high quality training to the image analysis community.

Although not a necessity, we recommend attending the Introduction to MATLAB course run at the Training Facility and / or the Intro to MATLAB using MathWorks prior to the workshop. For the MathsWorks course you will need to sign up for a MathWorks Account. All University of Cambridge members should be eligible to obtain a MathWorks Account. Create an account using your institution email address.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.

This module is shared with Psychology. Students from the Department of Psychology MUST book places on this course via the Department; any bookings made by Psychology students via the SSRMP portal will be cancelled.

The course focuses on practical hands-on variable handling and programming implementation using rather than on theory. This course is intended for those who have never programmed before, including those who only call/run Matlab scripts but are not familiar with how code works and how matrices are handled in Matlab. (Note that calling a couple of scripts is not 'real' programming.)

MATLAB (C) is a powerful scientific programming environment optimal for data analysis and engineering solutions. More information on the programme and its uses can be found here

More information on the course can be found here

Using the Parallel Computing capabilities in MATLAB allows you to take advantage of additional hardware resources that may be available either locally on your desktop or on clusters and clouds. By using more hardware, you can reduce the cycle time for your workflow and solve computationally- and data-intensive problems faster.

In this seminar, we will discuss a range of workflows available to scale MATLAB applications with minimal changes to your MATLAB code and without needing to learn any shell or scheduler programming syntax.

This course will provide a detailed critique of the methods and philosophy of the Null Hypothesis Significance Testing (NHST) approach to statistics which is currently dominant in social and biomedical science. We will briefly contrast NHST with alternatives, especially with Bayesian methods. We will use some computer code (Matlab and R) to demonstrate some issues. However, we will focus on the big picture rather on the implementation of specific procedures.

This course will focus on computational methods for analysing cellular images and extracting quantitative data from them. The aim of this course is to familiarise the participants with computational image analysis methodologies, and to provide hands-on training in running quantitative analysis pipelines.

On day 1 we will introduce principles of image processing and analysis, giving an overview of commonly used algorithms through a series of talks and practicals based on Fiji, an extensible open source software package.

On day 2, we will focus on machine learning and computer vision for the analysis of images in cell biology. We will introduce the methodology in a series of lectures and show their application in the hands-on session. These practical sessions will be based on CellCognition, a tool for the analysis of live cell imaging data.

On day 3, we will describe the open Icy platform developed at the Institut Pasteur. Icy is a next-generation, user-friendly software offering powerful acquisition, visualization, annotation and analysis algorithms for 5D bioimaging data, together with unique automation/scripting capabilities (notably via its graphical programming interface) and tight integration with existing software (e.g. ImageJ, Matlab, Micro-Manager).

The timetable can be found here.

This event is sponsored by the Systems Microscopy NoE.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to Book or register Interest by linking here.

This course will focus on computational methods for analysing cellular images and extracting quantitative data from them. The aim of this course is to familiarise the participants with computational image analysis methodologies, and to provide hands-on training in running quantitative analysis pipelines.

On day 1 we will introduce principles of image processing and analysis, giving an overview of commonly used algorithms through a series of talks and practicals based on Fiji, an extensible open source software package.

On day 2, we will cover time series processing and cell tracking using TrackMate and advanced image segmentation using Ilastik. Additionally, in the afternoon we will run a study design and data clinic (sign up will be required) for participants that wish to discuss their experiments.

On day 3, we will describe the open Icy platform developed at the Institut Pasteur. Icy is a next-generation, user-friendly software offering powerful acquisition, visualisation, annotation and analysis algorithms for 5D bioimaging data, together with unique automation/scripting capabilities (notably via its graphical programming interface) and tight integration with existing software (e.g. ImageJ, Matlab, Micro-Manager).

The training room is located on the first floor and there is currently no wheelchair or level access available to this level.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book or register your interest by linking here.

This course is part of the Scientific Computing series.

This course is aimed at those new to programming and provides an introduction to programming using Python, focussing on scientific programming. This course is probably unsuitable for those with programming experience, even if it is just in shell scripting or Matlab-like programs. By the end of this course, attendees should be able to write simple Python programs and to understand more complex Python programs written by others.

As this course is part of the Scientific Computing series, the examples chosen are of most relevance to scientific programming.

This course is organized by the Software Carpentry Project. Python will be used to explore program design, but we aim to cover principles that are applicable to other programming languages, so if you are an R or Matlab user you can also learn something useful. The course will include sessions covering command line UNIX, Version control with Git and programming, testing and debugging in Python.

Applicants for this course are required to complete the survey that can be found here. Applicants will be accepted on condition that their background and motivation fit the course content.

Further information is available here.

Please note that if you are not eligible for a University of Cambridge Raven account you will need to book by linking here.