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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.

C++: Programming in Modern C++ Wed 4 Jan 2017   09:30 Finished

This is an introduction to programming in modern C++, based on the book "'Programming: Principles and Practice using C++"' (2nd ed.) by Bjarne Stroustrup. The aim is to teach participants how to write non trivial, practical programs that are comprehensible and portable. Participants should also be able to understand and modify most well-written C++ applications, though not necessarily every aspect of them.

C++ is a large and complicated language, which is reflected in the length of this course. The creator of C++, Prof. Stroustrup, estimates that newcomers to programming will have to devote in excess of 200 hours' of work to learn how to program in C++ properly. Please bear that in mind if signing up for the course. It would also be of help (though not essential) if attendees have some prior programming experience in another language, e.g. Python.

High Performance Computing: An Introduction Wed 10 Jun 2020   09:30 CANCELLED

Bookings for this course are placed on hold in response to the rapidly changing events due to COVID-19. The status of these courses will be reviewed towards the end of April 2020. See the University's COVID-19 page for up to date information.

The course aims to give an introductory overview of High Performance Computing (HPC) in general, and of the facilities of the High Performance Computing Service (HPCS) in particular.

Practical examples of using the HPCS clusters will be used throughout, although it is hoped that much of the content will have applicability to systems elsewhere.

This course aims to provide a basic knowledge of GPU programming using OpenACC directives. The course is very hands-on oriented, aiming to give to you the opportunity to practice and experiment from the very beginning.

LiL: MATLAB - Learning MATLAB (Online) new Self-taught Booking not required

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

The course can be accessed here.

LiL: MATLAB 2018 Essential Training (Online) new Self-taught Booking not required

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

The 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.

MathWorks Academy: MATLAB Fundamentals (Online) new Self-taught Booking not required

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.

MathWorks Academy: MATLAB Onramp (Online) new Self-taught Booking not required

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.

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.

MathWorks Academy: MATLAB Onramp Stateflow (Online) new Self-taught Booking not required

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.

MathWorks Academy: MATLAB Simulink Onramp (Online) new Self-taught Booking not required

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.

MathWorks Academy: MATLAB: Deep Learning (Online) new Self-taught Booking not required

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.

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.

MathWorks Academy: MATLAB: Machine Learning (Online) new Self-taught Booking not required

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 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.

Mendeley for Researchers: introduction (Online) new Self-taught Booking not required

This is a course created by Mendeley and selected for curation by UIS training staff.

The course can be accessed here

This course provides an introduction to Photogrammetry, or the creation of three-dimensional (3D) models from two-dimensional (2D) data obtained from photographs, while introducing related 3D concepts and technologies. Along with basic 3D concepts (point clouds, meshes, file formats) there will be an overview and experience of creating and managing 3D images, which will provide a better understanding of the process of capturing 3D data from a variety of objects (from a stone to a city). Specialist software (e.g. Agisoft Metashape) will be introduced in order to build a 3D model from a series of photos, using a trial version of the product to produce and adjust your 3D model.

Exploring some of the competing methods of capturing 3D data in several fields, including 3D scanning and CT scanning, will be combined with an overview of preparing the results for use in research, in publications, and in VR/AR/XR projects. In addition, some useful sources of information and guidance will be provided, along with software and hardware tips and some of the key repositories used for sharing your models, or for making use of models made by others, or just for getting inspired. This course also will provide an opportunity to meet others who are already using or would like to use photogrammetry or other 3D technologies in their research or personal projects.

This course is part of the Scientific Computing series.

This course is aimed at those new to programming, or who have never been formally taught the principles and basic concepts of programming. It provides an introduction to the basic concepts common to most high level languages (including Python, Java, Fortran, C, C++, Visual Basic). The aim of the course is to equip attendees with the background knowledge and confidence necessary to tackle many on-line and printed programming tutorials. It may also help attendees in deciding which programming language is suitable for their programming task.

Knowledge of the concepts presented in this course is a pre-requisite for many of the other courses in the Scientific Computing series of courses (although not for the "Python for Absolute Beginners" course).

Python 3: Advanced Topics (Self-paced) Wed 11 Mar 2020   09:30 Finished

This course is part of the Scientific Computing series and is suitable for people who have Python experience equivalent to either of the introductory courses: Introduction for Absolute Beginners or Introduction for Programmers

These sessions consist of a selection of self-paced mini-courses, each taking at most a half-day. Python expert(s) from the UCS will be present to answer questions or address difficulties with these. Attendees can select from the available topics to most closely meet their individual needs. Attendees are welcome to attend more than one session to work through multiple topics. If an attendee finishes a topic with time to spare they may select another, and so on.

Python 3: Introduction for Absolute Beginners Mon 11 May 2020   14:00 CANCELLED

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.

Bookings for this course are placed on hold in response to the rapidly changing events due to COVID-19. The status of these courses will be reviewed towards the end of April 2020. See the University's COVID-19 page for up to date information.

This course is part of the Scientific Computing series.

This full-day course introduces the Python programming language to those who are already familiar with another high level programing language such as C/C++, Fortran, Java, Perl or Visual Basic. The aim of this course is to give such programmers sufficient familiarity with Python that they can attend any of the more advanced Python courses organised by the Computing service and easily follow any of the widely available Python tutorials on the more complex aspects of the language.

This course covers all the material contained in the "Programming: Python for Absolute Beginners" course, but in a more abbreviated fashion suitable for those who already have significant programming experience. This course does NOT cover the more complex aspects of the language (for such topics see the other Computing Service Python courses), nor is there much explicit discussion of the object oriented features of Python.

If you are an accomplished and experienced programmer you may find this course too slow, you may prefer to self-teach the course rather than attend in person, the full set of notes can be downloaded.

Research Computing: Infrastructure as a Service Thu 19 Mar 2020   09:30 CANCELLED

The Research Computing Infrastructure as a Service (RCIS) provides instant high performance compute, storage, network resources and other functionality. It helps avoid the expense and complexity of buying and managing your own physical servers and other data centre infrastructure. It enables IT practitioners and research groups to build their own scalable platforms that fit their exact needs and requirements. Departmental IT or Research Groups are able to submit an application here to rent a portion of the available cloud resources, on which to build their own research computing platforms without needing to first provision physical hardware in their home department.

Please register your interest in the course and we will be in touch when we have finalised dates.

Unix: Building, Installing and Running Software Mon 2 Mar 2020   14:00 Finished

This course is part of the Scientific Computing series.

It is common for a student or researcher to find a piece of software or to have one thrust upon them by a supervisor which they must then build, install and use. It is a myth that any of this requires system privilege. This course demonstrates the building, installation and use of typical software ranging from trivially easy examples (the "configure, make, install" scheme) through to the evils of badly written Makefiles. Common errors and what they mean will be covered and by the end of the course the student should be able to manage their own software without needing to pester their system administrator.

Bookings for this course are placed on hold in response to the rapidly changing events due to COVID-19. The status of these courses will be reviewed towards the end of April 2020. See the University's COVID-19 page for up to date information.

The course is designed to take someone from having no knowledge of the Unix command line to being able to navigate around directories, and doing simple file manipulation. Then some of the more basic commands, will be introduced, including information on how to get more help from the system itself. Finally accessing remote computers by ssh and the most basic of shell scripts will be introduced.

Unix: Simple Shell Scripting for Scientists Tue 16 Jun 2020   14:00 CANCELLED

Bookings for this course are placed on hold in response to the rapidly changing events due to COVID-19. The status of these courses will be reviewed towards the end of April 2020. See the University's COVID-19 page for up to date information.

This course is part of the Scientific Computing series.

No previous experience of shell scripting is required for this course; however some knowledge of the interactive use of the bash shell is a prerequisite (see Simple Shell Scripting for Scientists: Prerequisites for details).

This course introduces shell scripting in bash for scientific computing tasks. Day one introduces very basic shell scripts in bash which process the command line in a simple fashion. Day two covers how to write more advanced shell scripts in bash. Day three covers how to make one's shell scripts more robust.

At the end of each day one or more exercises are set. It is VERY IMPORTANT that attendees attempt these exercises before the next day of the course. Attendees should make sure that they have allowed themselves sufficient study time for these exercises between each day of the course.

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