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:
1) Understand and implement advanced supervised learning techniques such as CNNs, RNNs, Transformer Models, and Bayesian Machine Learning methods.
2) Understand and implement advanced unsupervised learning techniques including various clustering, dimension reduction, and anomaly detection methods.
3) Apply these techniques to real-world datasets and interpret the results.
4) 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@prstatistics.com with any questions
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
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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:
1) Understand and implement advanced supervised learning techniques such as CNNs, RNNs, Transformer Models, and Bayesian Machine Learning methods.
2) Understand and implement advanced unsupervised learning techniques including various clustering, dimension reduction, and anomaly detection methods.
3) Apply these techniques to real-world datasets and interpret the results.
4) 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@prstatistics.com with any questions