Artificial Intelligence & Machine Learning

Completed

Core AI and machine learning concepts, model training, evaluation, and understanding how systems learn from data.

What I Learned

I learned the core ML workflow: preparing data, selecting features, training models, and evaluating performance. I also developed a better understanding of overfitting, model generalisation, and why evaluation metrics matter.

Key Concepts

Learning from data (features, labels, training/testing)
Model generalisation vs overfitting
Model evaluation and comparison

Key Topics

Supervised learning
Unsupervised learning
Classification and regression
Training / validation / testing
Performance metrics
Bias, fairness, and ethics in AI

Practical Takeaways

Confidence building and evaluating basic machine learning pipelines
Better understanding of how to select methods for different problem types
Strong foundation for AI-related research and experimentation

Tools & Technologies

Python
Jupyter Notebook
NumPy
Pandas
scikit-learn
Data visualisation tools

References & Resources

University of York module materials (COM00143M)
scikit-learn documentation
Introductory AI/ML lecture notes and reading materials

Notes

This module is directly relevant to my later research interests, especially applying ML methods in security and audio-related problem domains.