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Venue: Room 5027 (5A) in the Maths Building (Number 54)
Programme
| Course 1: Bayesian modelling and computation |
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| Programme on June 11, Monday. |
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| 9AM--9:30AM | Welcome and Registration |
| 9:30AM--12:30PM | Morning Sessions |
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Coffee Break 11--11:30AM |
Session 1. Intro to Bayesian methods
Session 2. Bayesian computation
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| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM |
Session 3. Introduction to Gibbs sampling and WinBugs
Session 4. Practical Issues in MCMC.
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| Course 1: Programme on June 12, Tuesday. |
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| 9:30AM--12:30PM | Morning Sessions |
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Coffee Break 11--11:30AM |
Session 5. Bayesian model comparison
Session 6. Hands on coding of MCMC
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| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM |
Session 7.
Bayesian hierarchical modelling.
Session 8.
Other computing packages: STAN and INLA.
One-on-one and group brainstorming sessions with the instructors where
participants can discuss modelling their own data sets.
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| Participants can depart at 4:30PM. |
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| Course 2: Statistical Machine Learning |
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| Programme on June 13, Wednesday. |
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| 9--9:30AM | |
| 9:30AM--12:30PM | Morning Sessions |
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Coffee Break 11--11:30AM | 1. Supervised Learning
2. Regression – Multicollinearity, Variable selection, Regularisation, LASSO prior, Ridge prior, Elastic Net prior
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| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM |
1. Non-linear regression - Gaussian Process Prior Regression
2. Practical session.
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| Course 2: Programme on June 14, Thursday. |
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| 9:30AM--12:30PM | Morning Sessions |
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Coffee Break 11--11:30AM |
1. Classification
2. Naive Bayes classifier, Discriminant Analysis, logistic regression.
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| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM |
1. Support Vector Machine, Random Forest, Perceptron Learning, Neural Network, Deep Learning.
2. Participants can discuss their own modelling problems with the instructors. |
| Course 2: Programme on June 15, Friday. |
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| 9:30AM--12:30PM | Morning Sessions |
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Coffee Break 11--11:30AM |
1. Algorithms – Gradient Descent, Stochastic Gradient Descent, Back Propagation
2. Hands on session.
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| 12:30PM-1:30PM | Lunch Break |
| 1:30PM -4:30PM | Afternoon Sessions |
| Tea Break: 3--3:30PM |
1. Unsupervised learning.
2. K-means clustering, Principal Component Analysis and Latent Dirichlet Analysis. |
| Participants can depart at 4:30PM. |
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