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Machine Learning

Key Information

Course details
Compulsory foundation course for MSc, Michaelmas Term
Reading list
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Tutor
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

  1. Introduction to machine learning  and Regression
  2. Classification
  3. Boosting
  4. Training, Test, and Validation
  5. Algorithmic fairness
  6.  Neural networks
  7. Modern deep learning and computer vision
  8. Unsupervised learning (PCA, k-means)
  9. Language models and Zero-shot learning
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