[R-sig-genetics] ONLINE COURSE – Machine Learning with R (Intermediate – Advanced) (MLIA01)

[Oliver Hooker]

Not a genetic specific course but it might be of interest to some.

ONLINE COURSE – Machine Learning with R (Intermediate – Advanced) (MLIA01)

https://www.prstatistics.com/course/machine-learning-with-r-intermediate-advanced-mlia01/

28th - 31st August 2023

PLEASE FEEL FREE TO SHARE!
COURSE DETAILS - This intensive 4-day course provides an in-depth
exploration of machine learning using the popular open-source statistical
software, R. Participants are assumed to have a basic working knowledge of
regression and supervised learning techniques and so will gain a further
understanding of various intermediate and advanced machine learning
algorithms, how they work, and how to implement them using R’s ecosystem of
packages. Real-world data sets will be used to offer hands-on experience
and help participants understand the practical applications of the covered
concepts.

By the end of this course, students should be able to:

Understand and implement advanced supervised learning techniques such as
CNNs, RNNs, Transformer Models, and Bayesian Machine Learning methods.
Understand and implement advanced unsupervised learning techniques
including various clustering, dimension reduction, and anomaly detection
methods.
Apply these techniques to real-world datasets and interpret the results.
Understand the underlying methods and assumptions/drawbacks of these
techniques.

Day 1: Classes from 09:30 to 17:30 - Deep Dive into Supervised Learning
We begin with an introduction to Deep Learning in which we cover the basic
concepts and its difference from traditional machine learning. We then
extend to Convolutional Neural Networks (CNNs), exploring their
architecture, their use in image and video processing, and their role in
object detection and recognition. Finally we cover time series models
through Recurrent Neural Networks (RNNs) and their application in
sequential data analysis and natural language processing.
In the afternoon sessions we implement CNNs and RNNs using real data sets
R Packages used: keras, tensorflow


Day 2: Classes from 09:30 to 17:30 - Advanced Supervised Learning Techniques
On day 2 we cover Transformer models and Bayesian machine learning
techniques. We start by understanding the transformer architecture, its
self-attention mechanism, and its use in natural language processing tasks.
We then cover the basics of Bayesian inference and explore its use in
classification and regression tasks, and compare it to traditional machine
learning methods.
In the afternoon sessions the students can choose whether they explore
either the Transformer or Bayesian methods further by following and
extending some example R scripts.
R Packages: keras, tensorflow, rstan, brms, BART

Day 3: Classes from 09:30 to 17:30 - Unsupervised Learning – Clustering and
Dimension Reduction
The third day will focus on advanced clustering techniques and dimension
reduction. We start by exploring clustering techniques including
hierarchical clustering, DBSCAN, and their use in segmentation. We then
cover dimension reduction techniques; starting with PCA and extending to
t-SNE and UMAP. We explain how these techniques work and explore their use
in visualisation of data sets with high dimensions.
In the afternoon session students will explore the use of these techniques
through real-world data sets.
R Packages: cluster, dbscan, factoextra, Rtsne, umap

Day 4: Classes from 09:30 to 17:30
Unsupervised Learning – Anomaly Detection and Course Wrap-up
On the final day we will focus on anomaly detection techniques and bringing
together the topics covered throughout the course. We start with various
anomaly detection techniques and demonstrate their use in e.g. fraud
detection, network security, and health monitoring. We then provide a
discussion session where we review the content of the course and talk about
future steps in Machine Learning.
In the afternoon students have the opportunity to work on their own data
sets and ask questions of the course instructor.
R Packages: anomalize, forecast, e1071

Please email oliverhooker using prstatistics.com with any questions


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Oliver Hooker PhD.
PR statistics

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