Dr Chris Russell
Dieter Schwarz Associate Professor, AI, Government & Policy.
Chris Russell is the Dieter Schwarz Associate Professor, AI, Government and Policy. Dr Russell’s work lies at the intersection of computer vision and responsible AI.
Key Information
Course details
Compulsory foundation course for MSc, Michaelmas Term
Compulsory foundation course for MSc, Michaelmas Term
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Tutor
Dr Chris Russell
Dr Chris Russell
About
Machine learning algorithms can discover patterns and hidden structure in data and use these for prediction of future data. This course covers the fundamentals of both supervised and unsupervised learning. Machine learning has many applications in the social sciences and is considered a key data science method. Applications include clustering documents and latent attribute inference (trying to infer demographics, personality traits, or other attributes about a person from behavioural data).
Key Themes
- Supervised and unsupervised machine learning
- Regression and classification
- Overfitting and regularization
- Support vector machines and tree-based methods (random forests, boosting)
- Neural networks and modern deep learning
Learning Outcomes
At the end of this course students will…
Objectives
- understand what is meant by ‘machine learning’
- compare various machine learning methods and understand the benefits and limitations of each in reference to a given problem
- be able to obtain state-of-the-art performance on a wide range of tabular datasets
- understand how to manipulate algorithms to obtain more equitable outcomes
- be familiar with the key concepts of computer vision
Topics
- Introduction to machine learning and Regression
- Classification
- Boosting
- Training, Test, and Validation
- Algorithmic fairness
- Neural networks
- Modern deep learning and computer vision
- Unsupervised learning (PCA, k-means)
- Language models and Zero-shot learning
Course Tutor