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

[Oliver Hooker]

ONLINE COURSE – Machine Learning with R (Intermediate – Advanced) (MLIA01)
https://www.prstatistics.com/course/machine-learning-with-r-intermediate-advanced-mlia01/

4th - 8th December 2023

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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




-- 

Oliver Hooker PhD.
PR statistics

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