Prof Sujit K Sahu

          

   

Venue: Room 5027 (5A) in the Maths Building (Number 54)
Programme

Course 1: Bayesian Modelling and Computation
Programme on June 11, Monday.
9AM--9:30AM Welcome and Registration
9:30AM--12:30PM Morning Sessions
Coffee Break 11--11:30AM 1. Introduction to Bayesian methods, principles of Bayesian inference.
2. Bayesian computation (Introduction): Importance sampling, Monte Carlo sampling and integration; Gibbs sampling and MCMC.
12:30PM-1:30PM Lunch Break
1:30PM -4:30PM Afternoon Sessions
Tea Break: 3--3:30PM 1. Bayesian model comparison/model adequacy.
2. Hands on coding of the Gibbs sampler using R.
Course 1: Programme on June 12, Tuesday.
9:30AM--12:30PM Morning Sessions
Coffee Break 11--11:30AM 1. Bayesian Hierarchical modelling.
2. Introduction to WinBUGS.
12:30PM-1:30PM Lunch Break
1:30PM -4:30PM Afternoon Sessions
Tea Break: 3--3:30PM 1. Hands on session. Scripting in OpenBUGS and WinBUGS. Running winBUGS within R.
2. Discussion of other computing packages such as STAN and INLA.
3. One-on-one and group brainstorming sessions with the instructors where participants can discuss modelling their own data sets.
Participants can depart at 4:30PM.
Course 2: Hierarchical modelling of spatial and temporal data
Programme on June 13, Wednesday.
9--9:30AM
9:30AM--12:30PM Morning Sessions
Coffee Break 11--11:30AM 1. Supervised Learning
2. Regression – Multicollinearity, Variable selection, Regularisation, LASSO prior, Ridge prior, Elastic Net prior
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.
Course 2: Programme on June 14, Thursday.
9:30AM--12:30PM Morning Sessions
Coffee Break 11--11:30AM 1. Classification
2. Naive Bayes classifier, Discriminant Analysis, logistic regression.
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.
9:30AM--12:30PM Morning Sessions
Coffee Break 11--11:30AM 1. Algorithms – Gradient Descent, Stochastic Gradient Descent, Back Propagation
2. Hands on session.
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.

Mathematical Sciences | S3RI | University of Southampton
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