Preliminary Reading
Computing
- Python: https://www.python.org/
- An introduction to Python: https://notebooks.azure.com/garth-wells/projects/CUED-IA-Computing-Michaelmas
- An introduction to Scientific Computing in Python: https://github.com/damonjw/scicomp
- Natural Language Processing with Python: http://www.nltk.org/book/ and http://www.nltk.org/ (good introductions to Python for problems in text processing)
General Reading
- Nate Silver (2013). The Signal and the Noise: The Art and Science of Prediction. Penguin
- Sharon Bertsch Mcgrayne (2011).The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy. Yale University Press
Course Specific Reading
In the summer before students arrive we will send through some preparation work to be completed before term begins. However general reading which would be appropriate for the course includes:
- Bishop, C. Pattern Recognition and Machine Learning (2007).
- Jurafsky, D & Martin, J. (2008). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall (2nd ed.). Section II on ‘Speech’
- P. Taylor (2009). Text-to-Speech Synthesis, Cambridge University Press
- Murphy, K. P. (2012) Machine Learning: A probabilistic perspective. Chapters 1-8
- Barber, D (2007) Bayesian Reasoning and Machine Learning http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf
- MacKay, D J C (2003). Information Theory, Inference and Learning Algorithms, CUP; available free online at http://www.inference.eng.cam.ac.uk/mackay/itila/
- Ghahramani, Z. (2013) Bayesian nonparametrics and the probabilistic approach to modelling. Philosophical Transactions of the Royal Society A.