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Home › Recorded Courses › Machine Learning Intermediate to Advanced (MLIAPR)
MLIAPR

Machine Learning Intermediate to Advanced

Advance your machine learning skills in R with deep learning, Bayesian methods, transformer models, clustering, and anomaly detection in this 28-hour live online course.

  • Duration: 28 Hours
  • Format: Recorded ‘on-demand’ Format

£450Registration Fee

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5.0

from 200+ reviews

  • Overview
  • Instructors
  • Schedule

Course Description

This intensive 28 hour 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.

What You’ll Learn

During the course will cover the following:

  • 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.

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 academics and postgraduate students working on projects where advanced machine learning and predictive modelling techniques are valuable. Participants should have a basic understanding of statistical concepts, such as linear and logistic regression, as well as familiarity with core machine learning methods including Random Forests, Gradient Boosting, k-NN, and SVMs. A good working knowledge of R is required, including the ability to import and export data, manipulate data frames, fit basic machine learning models, and generate simple exploratory and diagnostic plots.

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

Prof. Andrew Parnell

Prof. Andrew Parnell

Andrew is a statistician and professor working at the intersection of statistics, machine learning, and real-world scientific applications. His research focuses on developing and applying statistical methods for large, structured datasets, with applications spanning climate science, 3D printing, bioinformatics, and more. He works with a wide array of techniques, including Bayesian hierarchical models, time series analysis, and modern machine learning tools.

Andrew holds the Hamilton Professorship of Statistics at the Hamilton Institute, Maynooth University. He has co-authored over 90 peer-reviewed publications in high-impact journals such as Science, Nature Communications, and PNAS, as well as in leading statistical journals including Statistics and Computing, The Annals of Applied Statistics, JCGS, and JRSS Series C. He has extensive experience teaching Bayesian statistics, statistical learning, and applied modelling across undergraduate, postgraduate, and doctoral levels.

 

Education & Career
• Hamilton Professor of Statistics, Hamilton Institute, Maynooth University
• PhD in Statistics (Bayesian Methods for Complex Data)
• Internationally published researcher with over 90 peer-reviewed papers
• Active collaborator with interdisciplinary teams in science and engineering

 

Research Focus
Andrew’s work is centred on statistical methodology and its integration with machine learning for complex, structured data. He is particularly interested in how Bayesian inference and scalable modelling techniques can enhance data-driven research in the natural sciences, engineering, and public policy.

 

Current Projects
• Hierarchical Bayesian models for environmental and ecological datasets
• Machine learning methods for analysing high-dimensional, structured data
• Time series modelling for dynamic systems in science and industry
• Statistical approaches to reproducible, transparent modelling practices

 

Professional Consultancy
Andrew collaborates widely across disciplines, providing expert statistical advice on model development, uncertainty quantification, and data analysis pipelines. His applied consulting includes climate modelling, bioinformatics, additive manufacturing, and data-driven public health initiatives.

 

Teaching & Skills
• Instructor in Bayesian statistics, time series modelling, and machine learning
• Strong advocate for reproducibility, open-source tools, and accessible education
• Skilled in R, Stan, JAGS, and statistical computing for large datasets
• Experienced mentor and workshop leader at all academic levels

 

Links
• ResearchGate
• Google Scholar
• ORCID
• LinkedIn
• GitHub

Session 1 – 03:30:00 – 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.

Session 2 – 03:30:00 – Practical – Implement CNNs and RNNs using real data sets (R Packages used: keras, tensorflow)

Session 3 – 03:30:00 – 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.

Session 4 – 03:30:00 – Students can explore either Transformer methods further by following and extending some example R scripts. (R Packages: keras, tensorflow, rstan, brms, BART)

Session 5 – 03:30:00 – Students explore either Bayesian methods further by following and extending some example R scripts. (R Packages: keras, tensorflow, rstan, brms, BART)

Session 6 – 03:30:00 – We 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.

Session 7 – 03:30:00 – Students will explore the use of these techniques through real-world data sets. (R Packages: cluster, dbscan, factoextra, Rtsne, umap)

Session 8 – 03:30:00 – The final session 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. (R Packages: anomalize, forecast, e1071)

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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MLIAPR RECORDED
MLIAPR RECORDED
£ 450.00
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£450.00
24th October 2036 - 26th October 2036
Recorded, United Kingdom
Planet Saturn.