Foundations of Machine Learning

In person 

The Foundations of Machine Learning course provides a comprehensive introduction to the essential concepts and methods of machine learning. Aimed at attendees with no previous knowledge of the topic, this course is designed to demystify the core ideas of machine learning while equipping participants with practical skills. Starting with an exploration of what machine learning is and how it integrates with exploratory data analysis (EDA), participants gain an understanding of the importance of data preparation and insights in the modelling process. The course introduces classification as a key machine learning task, covering the basics of logistic regression as a starting point for understanding predictive models. 

Building on this foundation, the course explores more advanced classification techniques, such as decision trees and k-nearest neighbours (k-NN), while also delving into regression methods. Along the way, participants are introduced to practical considerations in machine learning, including overfitting, evaluation metrics, and model selection. By the end of the course, learners will have a solid grasp of both the theoretical and applied aspects of machine learning, enabling them to approach real-world problems with confidence. 

In each of the two sessions, there will be practical elements with participants undertaking a range of tasks in either R or Python (knowledge of only one language is needed to undertake the course). For this, the University's Noteable service will be used to allow participants to quickly access the Python/R environments and get hands-on practice without technological constraints. For this, participants will need access to their own laptop/computer. 

This course will be taught by Somya Iqbal and Joy Lan. 

After taking part in this event, you may decide that you need some further help in applying what you have learnt to your research. If so, you can book a Data Surgery meeting with one of our training fellows. 

More details about Data Surgeries. 

Those who have registered to take part will receive an email with full details on how to get ready for this course. 

If you’re new to this training event format, or to CDCS training events in general, read more on what to expect from CDCS training. Here you will also find details of our cancellation and no-show policy, which applies to this event. 

Level  

This course requires some prior knowledge: 

•  Attendees are expected to be familiar with either R or Python, and the University service Noteable (instructors will guide you on how to set up)

It will be sufficient for students to have taken the Introduction to Programming with to R and RStudio course or Introduction to Programming with Python. 

Learning Outcomes 

• Demonstrate a knowledge of relevant packages and implementations of machine learning tools in either R or Python

• Be able to program short blocks of machine learning code in either R or Python, in order to run basic machine learning models

• Differentiate between supervised and unsupervised machine learning, and when one is appropriate than the other

Skills  

• An ability to select appropriate machine learning models for classification tasks

• To leverage the advantages and disadvantages of a model for answering questions about data

• An ability to interpret and evaluate outputs from machine learning models covered in the sessions

Explore More Training

• A Gentle Introduction to Causal Inference
• Data Analysis Workflow Design
• Digital Method of the Month: Machine Learning
• Digital Method of the Month. Statistics: How to pick the right method
• Explainable Machine Learning (XAI)
• Getting Started with Bayesian Statistics
• Getting Started with Data Analysis in Python
• Getting Started with Regression in R
• Linear Mixed Effects Modelling

Return to the Training Homepage to see other available events