ALL TRAINING COURSES
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Despite the promise of big data, inferences are often limited not by the size of data but rather by its systematic structure. Only by carefully modelling this structure can we take full advantage of the data — big data must be complemented with big models and the algorithms that can fit them. Stan is a
This two-day intensive course will equip you with the knowledge and tools to undertake a variety of tasks in a standard machine learning analytics pipeline. In addition to techniques such as cross-validation, training and testing our models we will examine a range of model building algorithms for both regression and classification supervised learning problems on top of an introduction to unsupervised learning.
Predicting the future is a tough problem. Some things, like lottery numbers, are inherently unpredictable. Others, like air temperatures and rainfall, are reasonably predictable. Time series analysis makes it possible to assess whether or not predictions are possible and, if they are, build a model which can generate informed predictions for the future with realistic estimates of uncertainty.
Python is a powerful, fast programming language that plays well with others, runs everywhere, is friendly and easy to learn. Its a general purpose language in nature, however, it has a large number of packages which allows it to be suitable for a variety of tasks from data analysis to web scraping. Pythons syntax is simple yet elegant making it easy to read and quick to develop in. This course will cover the basics of how the language works looking at the core of components Python language.
Deep learning is a cutting edge machine learning technique for classification and regression. In the past few years it has produced state-of-the-art results in fields such as image classification, natural language processing, bioinformatics and robotics. This course will cover the main ideas of deep learning, and how to implement it in practice with tensorflow: a software framework for efficient and scalable deep learning. We’ll introduce the high-level keras library, which allows deployment of standard neural networks with just a few lines of code, as well as the details of raw tensorflow, allowing a deeper level of customisation.
This course is aimed at statisticians and data scientists already familiar with a dynamic programming language (such as R, Python or Octave) who would like to learn how to use Scala. Scala is a free modern, powerful, strongly-typed, functional programming language, well-suited to statistical computing and data science applications. In particular, it is fast and efficient and is designed to easily exploit modern multi-core and distributed computing architectures.
Despite the promise of big data, inferences are often limited not by the size of data but rather by its systematic structure. Stan is a platform for facilitating this modelling, providing an expressive modelling language for specifying bespoke models and implementing state-of-the-art algorithms to draw subsequent Bayesian inferences.
This is a one-day intensive course on the R package Shiny. The course will be a mixture of lectures and computer practicals. Shiny allows you to create cutting-edge, interactive web-graphics. Regardless of your background, Shiny will enable you to present your data in new and innovative ways.
The benefit of using a programming language such as R is that we can automate repetitive tasks. This course covers the fundamental techniques such as functions, for loops and conditional expressions. By the end of this course, you will understand what these techniques are and when to use them.