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CIMLPR

A Comprehensive Introduction to Machine Learning

Learn machine learning in R with this comprehensive course. Covers clustering, regression, trees, neural networks, and more—fully online and hands-on.

  • Duration: 24 Hours
  • Format: Recorded ‘on-demand’ Format

£350Registration Fee

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5.0

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  • Overview
  • Instructors
  • Schedule

Course Description

In this 24 hour course, we provide a comprehensive practical and theoretical introduction to statistical machine learning using R. We start by introducing the concepts of supervised and unsupervised learning. We firstly explore unsupervised learning, and introduce k-means and hierarchical clustering, as well as principal components analysis. We then move to supervised learning methods and cover logistic regression and regularisation methods (such as ridge regression and the LASSO). After that, we introduce the k-nearest neighbours method, and classification and regression trees (CART). Finally, we explore extensions to CART, such as random forests and, if time allows, Bayesian additive regression trees (BART).

What You’ll Learn

During the course we will cover the following:

  • Differentiatebetween supervised and unsupervised learning approaches and apply appropriate visualization techniques for classification and clustering tasks.
  • Implement unsupervised learning methods, including hierarchical clustering and k-means, to explore and group data.
  • Apply dimension reduction techniques such as principal components analysis (PCA) to simplify high-dimensional datasets.
  • Develop supervised learning models using linear and logistic regression for prediction and classification problems.
  • Construct and evaluate tree-based methods, including CART and random forests, for regression and classification tasks.
  • Extend tree-based approaches using advanced methods such as Bayesian additive regression trees (BART) and Boruta, and integrate them with parametric frameworks.
  • Utilize generalized additive models (GAM) and cross-validation techniques for flexible, robust model fitting and assessment.
  • Build and interpret neural network models within R, select network architectures using model selection techniques, and integrate them with GAMLSS to address complex modeling challenges.

Course Format

Flexible Learning Structure

Learn through a carefully structured mix of lecture recordings and guided exercises that you can pause, revisit, and complete at your own pace—ideal for busy professionals or those balancing multiple commitments.

Access Anytime, Anywhere

All course content is available on-demand, making it accessible across all time zones without the need to attend live sessions or adjust your schedule.

Independent Exploration with Support

Engage deeply with course topics through self-directed study, with the option to reach out to instructors via email for clarification or deeper discussion.

Comprehensive Learning Resources

Gain full access to the same high-quality materials provided in live sessions, including code, datasets, and presentation slides—all available to download and keep. Please note recordings can only be streamed.

Work With Your Own Data, On Your Terms

Apply what you learn directly to your own data projects as you go, allowing for a personalized and immediately practical learning experience.

Continued Guidance and Resource Access

Receive 30 days of post-enrolment email support and unrestricted access to all session recordings during that time, so you can review and reinforce your learning as needed.

Who Should Attend / Intended Audiences

This course is designed for anyone interested in statistical machine learning methods for clustering, classification, and prediction, with a focus on using R for data science and statistics. R is widely applied across academic research as well as in public and private sectors, making it an essential tool for modern data analysis. Participants are expected to have a basic understanding of R and core statistical concepts, including linear regression models, statistical significance, and hypothesis testing. They should also be familiar with importing and exporting data, manipulating data frames, fitting basic statistical models, and producing exploratory and diagnostic plots in R.

Equipment and Software requirements

A laptop or desktop computer with a functioning installation of R and RStudio is required. Both R and RStudio are free, open-source programs compatible with Windows, macOS, and Linux systems.

While not essential, using a large monitor—or ideally a dual-monitor setup—can significantly enhance your learning experience by allowing you to view course materials and work in R simultaneously.

All necessary R packages will be introduced and installed during the workshop.

Download R Download RStudio Download Zoom

Dr. Rafael De Andrade Moral

Dr. Rafael De Andrade Moral

Rafael is a statistician working at the intersection of ecological science, environmental research, and applied statistical modelling. His work focuses on developing and applying statistical and mathematical tools to understand ecological dynamics, improve wildlife management strategies, and support sustainable agricultural and environmental practices. With a strong foundation in both biology and statistics, Rafael’s research spans areas such as hierarchical modelling, population dynamics, and the integration of ecological theory with real-world data.
Rafael holds a PhD in Statistics from the University of São Paulo, building on an undergraduate background in Biology. He is currently an Associate Professor of Statistics at Maynooth University, Ireland, where he also leads the Theoretical and Statistical Ecology Group — a multidisciplinary research hub dedicated to advancing quantitative ecology.
In addition to his academic work, Rafael is deeply invested in science communication and innovative teaching. He produces educational music videos and statistical parodies, using creative media to make statistical concepts more engaging and accessible to students and the public alike.

 

Education & Career
• PhD in Statistics – University of São Paulo
• BSc in Biology
• Associate Professor of Statistics – Maynooth University
• Director – Theoretical and Statistical Ecology Group

 

Research Focus
Rafael’s research is rooted in ecological and environmental statistics, particularly:
• Statistical modelling of species distributions and abundance
• Applications of Bayesian and hierarchical models in wildlife and agricultural contexts
• Integrative approaches combining field data, simulation, and theory to inform policy and conservation
• Methodological innovation in data-poor or complex ecological systems

 

Current Projects
• Statistical methods for population modelling and biodiversity monitoring
• Quantitative frameworks for wildlife management under uncertainty
• Modelling ecological responses to climate and land-use changes
• Public outreach through creative science communication in Statistics

 

Professional Activites
Rafael collaborates widely with ecologists, conservationists, and agricultural scientists, providing expert statistical input on study design, modelling, and data analysis. He also supervises postgraduate research across interdisciplinary projects in quantitative ecology.

 

Teaching & Skills
• Teaches courses in statistical modelling, environmental statistics, and data analysis in R
• Promotes engaging and inclusive teaching practices, including music-based educational content
• Advocates for open science, reproducibility, and the integration of theory with application

 

Links
• ResearchGate
• Google Scholar
• ORCID
• GitHub

Session 1 – 02:00:00 – Introductory concepts in statistical machine learning.

Unsupervised vs. supervised learning. Useful plots in classification and clustering tasks.

 

Session 2 – 02:00:00 – Unsupervised learning methods: Hierarchical clustering and the k-means method.

 

Session 3 – 02:00:00 – Dimension reduction techniques and principal components analysis.

 

Session 4 – 02:00:00 – Regression and classification tasks. Supervised learning methods: linear and logistic regression.

 

Session 5 – 02:00:00 – Tree-based methods. Classification and regression trees (CART), random forests.

 

Session 6 – 02:00:00 – Extensions to tree-based methods. Bayesian additive regression trees (BART). Combining tree-based methods with a parametric regression framework.

 

Session 7 – 04:00:00 – Generalized additive models and cross-validation techniques.

Session 8 – 02:00:00 – Tree-based methods. Classification and regression trees (CART), random forests.

Session 9 – 02:00:00 – Extensions to tree-based methods. Bayesian additive regression trees (BART). Boruta.

Session 10 – 02:00:00 – Neural networks. Fitting feedforward neural networks and multilayer perceptron using R. Selecting the number of neurons based on cross-validation and information criteria. Neural networks as statistical models.

Session 11 – 02:00:00 – Generalized additive models for location, scale, and shape (GAMLSS). Combining regression trees and neural networks within the GAMLSS regression framework.

Testimonials

PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.

Rolando O. Santos

PhD Assistant Professor, Florida International University

Courses attended

SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07

Testimonials

PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.

Rolando O. Santos

PhD Assistant Professor, Florida International University

Courses attended

SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07

Testimonials

PRStats offers a great lineup of courses on statistical and analytical methods that are super relevant for ecologists and biologists. My lab and I have taken several of their courses—like Bayesian mixing models, time series analysis, and machine/deep learning—and we've found them very informative and directly useful for our work. I often recommend PRStats to my students and colleagues as a great way to brush up on or learn new R-based statistical skills.

Rolando O. Santos

PhD Assistant Professor, Florida International University

Courses attended

SIMM05, IMDL03, ITSA02, GEEE01 and MOVE07

Frequently asked questions

Everything you need to know about the product and billing.

When will I receive instructions on how to join?

You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.

Do I need administrator rights on my computer?

Yes — administrator access is recommended, as you may need to install software during the course. If you don’t have admin rights, please contact us before the course begins and we’ll provide a list of software to install manually.

I’m attending the course live — will I also get access to the session recordings?

Yes. All participants will receive access to the recordings for 30 days after the course ends.

I can’t attend every live session — can I join some sessions live and catch up on others later?

Absolutely. You’re welcome to join the live sessions you can and use the recordings for those you miss. We do encourage attending live if possible, as it gives you the chance to ask questions and interact with the instructor. You’re also welcome to send questions by email after the sessions.

I’m in a different time zone and plan to follow the course via recordings. When will these be available?

We aim to upload recordings on the same day, but occasionally they may be available the following day.

I can’t attend live — how can I ask questions?

You can email the instructor with any questions. For more complex topics, we’re happy to arrange a short Zoom call at a time that works for both of you.

Will I receive a certificate?

Yes. All participants receive a digital certificate of attendance, which includes the course title, number of hours, course dates, and the instructor’s name.

When will I receive instructions on how to join?

You’ll receive an email on the Friday before the course begins, with full instructions on how to join via Zoom. Please ensure you have Zoom installed in advance.

Do I need administrator rights on my computer?

Yes — administrator access is recommended, as you may need to install software during the course. If you don’t have admin rights, please contact us before the course begins and we’ll provide a list of software to install manually.

I’m attending the course live — will I also get access to the session recordings?

Yes. All participants will receive access to the recordings for 30 days after the course ends.

I can’t attend every live session — can I join some sessions live and catch up on others later?

Absolutely. You’re welcome to join the live sessions you can and use the recordings for those you miss. We do encourage attending live if possible, as it gives you the chance to ask questions and interact with the instructor. You’re also welcome to send questions by email after the sessions.

I’m in a different time zone and plan to follow the course via recordings. When will these be available?

We aim to upload recordings on the same day, but occasionally they may be available the following day.

I can’t attend live — how can I ask questions?

You can email the instructor with any questions. For more complex topics, we’re happy to arrange a short Zoom call at a time that works for both of you.

Will I receive a certificate?

Yes. All participants receive a digital certificate of attendance, which includes the course title, number of hours, course dates, and the instructor’s name.

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£ 350.00
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£350.00
10th August 2035 - 12th August 2035
Recorded, United Kingdom
Saturn and its rings