Course dates – MT – week beginning Monday 26th November 2018 – for Year 1 students
Andrew Zisserman and Andrea Vedaldi
- Discriminative learning
- Energy based models and max-margin learning.
- Advanced SVMs and kernel methods
- Basic stochastic optimization
- Random forests
- Neural networks, backprop, max-margin revisited for transfer learning
- Advanced stochastic optimization
- Convnets and their application to language, vision and speech
- Structured output CRFs and SVMs.
- Basic linear algebra
- Christopher M. Bishop, "Pattern Recognition and Machine Learning", Springer (2006), ISBN 0-38-731073-8.
- Hastie, Tibshirani, Friedman, "Elements of Statistical Learning", Second Edition, Springer, 2009. Pdf available online.
- Practical on large scale classification
- Practical on Convnets and their applications