Summary and Schedule
This is a new lesson built with The Carpentries Workbench.
A beginner-level introduction to the landscape of Artificial Intelligence (AI) techniques. This course will help researchers build a foundational understanding of key areas within AI, including machine learning, deep learning, and large language models (LLMs) including what they are, how they work, and how they relate to one another within the broader AI ecosystem. Designed for researchers with little or no prior knowledge of AI, this course provides clear, accessible explanations without requiring any coding or statistics experience.
These training materials were developed by the Southampton Research Software Group. The development of this course was funded through the EPSRC Doctoral Landscape Award EP/Z534894/1 2025 additional skills funding underpinning the pipeline for AI skills.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Introduction | |
| Duration: 00h 12m | 2. What is Artificial Intelligence? |
What is AI? What are some categories of AI? Which factors have contributed to the AI boom over the last few years? |
| Duration: 00h 39m | 3. Machine Learning - Teaching Computers from Data |
How is machine learning different from traditional programming? What are supervised, unsupervised, and reinforcement learning? What does it mean to train and test a model? What is overfitting, and why does it matter? When is machine learning the right choice and when is it not? |
| Duration: 01h 04m | 4. Deep Learning and Neural Networks |
What is an artificial neural network? What does “deep” mean in deep learning? How do neural networks learn from errors? Why does deep learning require substantial data and computing resources? |
| Duration: 01h 16m | 5. Large Language Models |
What is a large language model and how does it relate to deep
learning? How are LLMs trained, and what does “large” actually mean? Why do LLMs sometimes produce confident but incorrect information? What are the key limitations I should understand before using an LLM in my research? |
| Duration: 01h 46m | 6. AI in Research |
How are AI techniques being used across research disciplines
today? What questions should I ask before adopting an AI tool in my research workflow? What ethical responsibilities do I have as a researcher using AI? How do I handle transparency and reproducibility when AI has been part of my methodology? |
| Duration: 02h 11m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
No software or datasets are required for this workshop.