2016/17 Term 2:

Chapters refer to "The Elements of Statistical Learning" by Hastie et al.

• Week 3 - 23rd January - Organisational Meeting (Murray Pollock)
• Week 4 - 30th January - Chapter 2 - David Selby
• Week 5 - 6th February - Chapter 2 - Sherman Ip
• Week 6 - 13th February - Chapter 2 - Cyril Chimisov
• Week 7 - 20th February - Chapter 3 - Ale Avalos
• Week 8 - 27th February - Chapter 3 (Section 3.4) - Valerio Perrone
• Week 9 - 6th March - Lewis Rendell
• Week 10 - 13th March - Introduction to Random Forests (special session) - Christian Robert

2016/17 Term 3:

Chapters refer to "The Elements of Statistical Learning" by Hastie et al.

• Week 1 - 24th April - Chapter 4 - Seb Armstrong
• Week 2 - 1st May - bank holiday
• Week 3 - 8th May - Chapter 4 - Murray Pollock
• Week 4 - 15th May - Chapter 4 (Logistic Regression) - Sherman Ip
• Week 5 - 22nd May - Chapter 4 (Perceptron) - Sherman Ip
• Week 6 - 29th May - bank holiday
• Week 7 - 5th June - Chapter 5 (Splines) - Matt Moores
• Week 8 - 12th June - Chapter 5 (Wavelets) - Joe Meagher
• Week 9 - 19th June - Chapter 5 (RKHS) - Sigurd Assing
• Week 10 - 26th June - Chapter 5 (RKHS) - Sigurd Assing

2017/18 Term 1:

Chapters refer to "The Elements of Statistical Learning" by Hastie et al.

• Week 1 - 5th October - Schedule
• Week 2 - 12th October - Chapter 5 (RKHS) - Sigurd Assing
• Week 3 - 19th October - Chapter 5 (RKHS) - Sigurd Assing
• Week 4 - 26th October - Chapter 6 (Kernel Smoothing Methods) - Nick Tawn
• Week 5 - 2nd November - Chapter 6 (Kernel Smoothing Methods) - Nick Tawn
• Week 6 - 9th November - Seminar (Hengjian Jia and Cyril Chimisov)
• Week 7 - 16th November - Chapter 7 (Model Assessment and Selection) - Sherman Ip
• Week 8 - 23rd November - Chapter 7 (Model Assessment and Selection) - Sherman Ip
• Week 9 - 30th November -Chapter 7 (Model Assessment and Selection) - Sherman Ip
• Week 10 - 7th December - Chapter 7 (Bootstrapping) - Sigurd Assing
• Week 11 - 14th December - On Shrinkage for Smoothing Splines - Sigurd Assing

2017/18 Term 2:

Chapters refer to "The Elements of Statistical Learning" by Hastie et al.

• Week 1 - 11th January -Chapter 7 (Bootstrapping) - Sigurd Assing
• Week 2 - 18th January - Chapter 8 (Model Inference and Averaging) - Nick Tawn
• Week 3 - 25th January- Cancelled
• Week 4 - 1st February - Chapter 8 (Model Inference and Averaging) - Nick Tawn
• Week 5 - 8th February - Cancelled
• Week 6 - 15th February - Chapter 11 (Neural Networks) - Henry Jia
• Week 7 - 22nd February - Chapter 11 (Neural Networks) - Henry Jia
• Week 8 - 1st March - SOTA Methods and Data Sets (Neural Networks) - Henry Jia
• Week 9 - 8th March - Chapter 9 (Generalized Additive Models) - Sherman Ip
• Week 10 - 15th March - Chapter 9 (Tree-Based Methods) - Joe Meagher