transmittingscience
Statistics & Bioinformatics

Introduction to Machine Learning in R

Learn the logic behind predictive algorithms and apply them to your own biological datasets. Stop treating Machine Learning as a "black box".

  • No advanced mathematical background required.
  • BYOD: Work with your own data in the afternoons.
  • Official Certificate (approx. 40 hours workload).
  • Taught by Dr. Alain Zuur (Highland Statistics).
Dates Jan 20-31, 2025
Schedule (CET) 14:00 - 18:00
Software R & RStudio
Places Max. 20
Regular Fee
588 € (PhD)
Register Now

⛔ The Challenge

Modern biology generates massive, complex datasets. Traditional statistical methods often fail to capture non-linear patterns. Many researchers avoid Machine Learning because they fear the complex mathematics behind it, treating algorithms as a "black box" they can't explain in their papers.

✅ What you will achieve

  • Understand the logic of Random Forest, SVM & Regression Trees.
  • Learn to handle missing values and outliers in biological data.
  • Leave the course with R scripts applied to your own research data.

More than just watching videos

Our "By Researchers, For Researchers" philosophy ensures you leave with results, not just notes.

01

Live Theory & Logic

Mornings are dedicated to explaining the concepts behind the algorithms and the R syntax, avoiding heavy math jargon.

02

Hands-on Practice

You will replicate the instructor's scripts using provided datasets to ensure you master the technique.

03

BYOD Sessions

The core of TS: Afternoons are for working with Your Own Data under instructor supervision.

Software & Requirements

Participants should have basic knowledge of R and RStudio (data import, basic manipulation). Hardware requirements: Webcam, microphone, and good internet connection for live sessions.

Instructors

Dr. Alain Zuur

Highland Statistics Ltd. Statistical consultant for ecological data. Author of "A Beginner's Guide to R".

Dr. Elena Ieno

Highland Statistics Ltd. Biologist and senior statistician specialized in teaching R.

Course Program

Schedule: 14:00 - 18:00 (Madrid Time)

Day 1: Introduction & Data Prep
Supervised vs Unsupervised learning. Handling missing values.
Day 2: Trees & Forests
Regression Trees logic. Pruning. Introduction to Random Forest.
Day 3: Advanced RF
Optimizing parameters. Variable importance plots.
Day 4-10: SVM & Projects
Support Vector Machines. BYOD Sessions.
📄 Download Full Program & Bibliography (PDF)

Includes detailed hourly schedule and reading list.

Past participants from these institutions have trusted Transmitting Science:
[LOGO HARVARD] [LOGO CSIC] [LOGO CAMBRIDGE] [LOGO CNRS] [LOGO MAX PLANCK]

Registration Fees

Includes attendance, materials, official certificate and recordings (limited time).

PhD Student

Regular Fee
588 €
  • Access to live sessions.
  • Official ECTS Certificate.
  • BYOD consultancy sessions.
Select PhD

Postdoc / Professional

Regular Fee
741 €
  • Access to live sessions.
  • Official ECTS Certificate.
  • BYOD consultancy sessions.
  • Priority support.
Select Professional
Supported by / In collaboration with
[LOGO SOCIEDAD EVOLUTIVA] [LOGO SOCIEDAD PALEONTOLOGÍA] [LOGO BIOLOGY ASSOCIATION]

* Members of these societies may benefit from a 20% discount.

Frequently Asked Questions

Do you provide ECTS credits?
Yes. Upon completion, you will receive an official certificate detailing the workload (approx. 40 hours). Most universities recognize this as ECTS credits for PhD programs. Check with your graduate school.
I live in a different Timezone. Are sessions recorded?
Yes, theory sessions are recorded and available for a limited time. However, to obtain the certificate, live attendance to practical sessions (BYOD) is usually mandatory.
Can my University/Lab pay for the course?
Absolutely. We can issue an invoice addressed to your institution. During the registration process below, select "My institution pays" and fill in the billing details.
What if I need to cancel my registration?
We offer a flexible cancellation policy. If you cancel up to 2 weeks before the course starts, we refund 100% of the fee (minus administrative costs). Check our Terms & Conditions for details.
Do I need to be an expert in R?
No. You just need a basic working knowledge of R (how to import data and basic syntax). We build the advanced scripts together from scratch.
How does the "BYOD" (Bring Your Own Data) work?
In the afternoon sessions, you will open your own datasets. The instructors will move between "virtual rooms" to help you apply the morning's code to your specific research problem.

Secure Registration

1. Personal Information
2. Professional Status
3. Billing Details
4. Payment