ONLINE COURSE – Introduction to Machine Learning and Deep Learning using R (IMDL02) This course will be delivered live
17 November 2021 - 18 November 2021£275.00
This course will now be delivered live by video link in light of travel restrictions due to the COVID-19 (Coronavirus) outbreak.
This is a ‘LIVE COURSE’ – the instructor will be delivering lectures and coaching attendees through the accompanying computer practical’s via video link, a good internet connection is essential.
TIME ZONE – UK local time (GMT+1) – however all sessions will be recorded and made available allowing attendees from different time zones to follow a day behind with an additional 1/2 days support after the official course finish date (please email firstname.lastname@example.org for full details or to discuss how we can accommodate you).
In this two day course, we provide an introduction to machine learning and deep learning using R. We begin by
providing an overview of the machine learning and deep learning landscape, and then turn to some major machine
learning applications. We begin with binary and multiclass classification problems, then look at decision trees and
random forests, then look at unsupervised learning methods, all of which are major topics in machine learning. We then
cover artificial neural networks and deep learning. For this, we will use the powerful TensorFlow and Keras deep
learning toolboxes. As examples of deep learning nets, we will cover the relatively easy to understand multilayer
perceptron and then turn to convolutional neural networks.
THIS IS ONE COURSE IN OUR R SERIES – LOOK OUT FOR COURSES WITH THE SAME COURSE IMAGE TO FIND MORE IN THIS SERIES
This course is aimed at anyone who is interested in learning the machine learning or deep learning using R, both of
which have major applications both in industry and in academia.
Venue – Delivered remotely
Time zone – GMT+1
Availability – TBC
Duration – 2 days
Contact hours – Approx. 15 hours
ECT’s – Equal to 1 ECT’s
Language – English
PLEASE READ – CANCELLATION POLICY: Cancellations are accepted up to 28 days before the course start date subject to a 25% cancellation fee. Cancellations later than this may be considered, contact email@example.com. Failure to attend will result in the full cost of the course being charged. In the unfortunate event that a course is cancelled due to unforeseen circumstances a full refund of the course fees will be credited.
Dr. Mark Andrews
Works at – Senior Lecturer, Psychology Department, Nottingham Trent University, England
Teaches – Introduction to statistics using R and Rstudio; Introduction data visualization using GG plot 2; Introduction data wrangling using R and Rstudio; Introduction to generalised linear models using R and Rstudio; Introduction to mixed models using R an d Rstudio; Introduction to Bayesian data analysis for social and behavioural sciences using R and Stan; Structural Equation Models, Path Analysis, Causal Modelling and Latent Variable Models Using R; Generalised Linear, Nonlinear and General Additive Models; Python for data science, machine learning, and scientific computing
Mark Andrews is a Senior Lecturer in the Psychology Department at Nottingham Trent University in Nottingham, England. Mark is a graduate of the National University of Ireland and obtained an MA and PhD from Cornell University in New York. Mark’s research focuses on developing and testing Bayesian models of human cognition, with particular focus on human language processing and human memory. Mark’s research also focuses on general Bayesian data analysis, particularly as applied to data from the social and behavioural sciences. Since 2015, he and his colleague Professor Thom Baguley have been funded by the UK’s ESRC funding body to provide intensive workshops on Bayesian data analysis for researchers in the social sciences.
This course will be largely practical, hands-on, and workshop based. For each topic, there will first be some lecture style presentation, i.e., using slides or blackboard, to introduce and explain key concepts and theories. Then, we will cover how to perform the various statistical analyses using R. Any code that the instructor produces during these sessions will be uploaded to a publicly available GitHub site after each session. For the breaks between sessions, and between days, optional exercises will be provided. Solutions to these exercises and brief discussions of them will take place after each break.
The course will take place online using Zoom. On each day, the live video broadcasts will occur during UK local time (GMT+0) at:
All sessions will be video recorded and made available to all attendees as soon as possible, hopefully soon after each 2hr session.
If some sessions are not at a convenient time due to different time zones, attendees are encouraged to join as many of the live broadcasts as possible. For example, attendees from North America may be able to join the live sessions from 3pm-5pm and 6pm-8pm, and then catch up with the 12pm-2pm recorded session once it is uploaded. By joining any live sessions that are possible will allow attendees to benefit from asking questions and having discussions, rather than just watching prerecorded sessions.
At the start of the first day, we will ensure that everyone is comfortable with how Zoom works, and we’ll discuss the procedure for asking questions and raising comments.
Although not strictly required, using a large monitor or preferably even a second monitor will make the learning experience better, as you will be able to see my RStudio and your own RStudio simultaneously.
All the sessions will be video recorded, and made available immediately on a private video hosting website. Any materials, such as slides, data sets, etc., will be shared via GitHub.
Assumed quantitative knowledge
We will assume familiarity with some general statistical and mathematical concepts such as matrix algebra, calculus, probability distributions. However, expertise with these concepts are not necessary. Anyone who has taken any undergraduate (Bachelor’s) level course in mathematics, or even advanced high school level, can be assumed to have sufficient familiarity with these concepts.
Assumed computer background
We assume general familiarity with using R and RStudio, and some familiarity with programming in R, such as writing
Equipment and software requirements
Attendees of the course must use a computer with R/RStudio installed, as well as the necessary additional R packages. Instructions on how to install and configure all the software will be provided before the start of the course.
UNSURE ABOUT SUITABLILITY THEN PLEASE ASK firstname.lastname@example.org
Wednesday 17th – Classes from 12:00 to 20:00
Topic 1: Machine learning and Deep Learning Landscape. Concepts like machine learning, deep learning, big data,
data science have become widely used and celebrated in the last 10 years. However, their definitions are
relatively nebulous, and how they related to one another and to major fields like artificial intelligence and
general statistics are not simple matters. In this introduction, we briefly overview the field of machine learning
and deep learning, discussing its major characteristics and sub-topics.
Topic 2: Classification problems. Classification problems is one of the bread and butter topics in machine
learning, and is usually the first topic covered in introductions to machine learning. Here, we will cover image
classification (itself a “hello world” type problem in machine learning), and particularly focus on logistic
regression and support vector machines (SVMs).
Topic 3: Decision trees and random forests. Decision trees are a widely used machine learning method, particularly for classification. Random forests are an ensemble learning extension of decision trees whereby large number of decision tree classifiers are aggregated, which often leads to much improved performance.
Thursday 18th – Classes from 12:00 to 20:00
Topic 4: Unsupervised machine learning. Unsupervised learning is essentially a method of finding patterns in
unclassified data. Here, we will look at two of the most widely used unsupervised techniques: k-means
clustering and probabilistic mixture models.
Topic 5: Introducing artificial neural networks and deep learning. R provides many packages for artificial neural
networks and deep learning. These include Keras and TensorFlow, which are in fact interfaces to Python
packages. These are the most widely used major packages for deep learning in R. More recently, native support
for deep learning using R via Torch has been introduced. We will discuss this, but our focus will be on Keras and
TensorFlow given that widespread use.
Topic 6: Multilayer perceptons. Multilayer perceptrons are very powerful, yet relatively easy to understand,
artificial neural networks. They are also the simplest type of deep learning model. Here, we will build and train a multilayer perceptron for a classification problem.
Topic 7: Convolutional neural networks. Convolutional neural networks (CNNs) have proved high successful at
image classification, primarily due to their translation invariance, which is highly suitable for computational
vision. Here, we use PyTorch to build and train a CNN for image classification.