Teaching


Current academic year...


[for the syllabus πŸ”— ]

Machine learning and neural networks III (MATH3431)
Epiphany
(stochastic gradient, support vector machines, kernelised models, GP, neural networks)
Description πŸ”— ; Handouts πŸ”— ; Exercise sheet πŸ”— ; Computer practicals πŸ”—

Spatio-temporal statistics IV (MATH4341)
Michaelmas
(point referenced data analysis; areal data analysis; point pattern data analysis; INLA)
Description πŸ”—Β  ;Β  Handouts πŸ”— ;Β  Exercise sheet πŸ”— ;Β  Problem classes πŸ”—


Β Β  BSc, MMath, MSc project / dissertation general topics...


BSc/MMath Project III/IV (MATH3382/MATH4072)Β 
  • Machine Learning for classification and clustering; methods & applications πŸ”—
    2023/24
  • Machine Learning for classification and clustering; methods, theory, & applications πŸ”—
    2023/24
  • Bayesian hierarchical modeling and analysis of spatial-temporal data πŸ”—
    2020/21

  • Statistical techniques and models for the analysis of functional dataΒ  πŸ”—
    2021/22

  • Bayesian hierarchical modeling and analysis of spatial data πŸ”—
    2020/21

  • Bayesian computational methodsΒ  πŸ”—
    2019

  • Bayesian statistics under model uncertainty and computations πŸ”—
    2020/21

  • Bayesian global optimisation πŸ”—
    2017/18

MSc Scientific Computing and Data Analysis (G5K609)
  • Statistical and Machine Learning methods for classification and clustering πŸ”—
    2022
  • Bayesian hierarchical modeling and analysis of spatial and spatio-temporal dataΒ  πŸ”—
    2021

  • Calibration of computer models with Bayesian global optimizationΒ  πŸ”—
    2020

MDS Master of Data Science (G5K823)
  • Geostatistical methods for the analysis of spatial data πŸ”—
    2021



Summer research projects / internship topics...


London Mathematical Society undergrad research project
  • Bayesian computational methods for big data
    July & August, 2020
    • @ Department of Mathematical sciences, Durham University, UK
  • Gaussian processes emulation in big data
    July & August, 2017
    • @ Department of Mathematical sciences, Durham University, UK

International Association for the Exchange of Students for Technical Experience

  • Monte Carlo methods for big data
    July & August, 2017
    • @ Department of Mathematical sciences, Durham University, UK


Courses taught ...


MMath Master of Mathematics (G103) ; Mathematics and Statistics (G114)
BSc in Mathematical Sciences (G100) ; Mathematics and Statistics (G111)
Department of Mathematical sciences, Durham University, UK

***
[for the syllabus πŸ”— ]

Undergraduate levels III, IV & postgraduate

Bayesian statistics III/IV/V (MATH3341/MATH4031/MATH43220)Β 
Michaelmas
(inference & methods)
Description πŸ”—Β  ;Β  Handouts πŸ”— ;Β  Exercise sheet πŸ”— ;Β  Computer practicals πŸ”—

Bayesian statistics III/IV/V (MATH3341/MATH4031/MATH43220)Β 
Epiphany
(MCMC & computations)
  • Teaching material available from Blackboard ULTRA
    2022

Topics in Statistics III/IV (MATH3361/MATH4071)Β 
Michaelmas
(categorical data analysis, likelihood methods & math stats)
Contingency tables: Β  Β  Β  Β  Β  Β  Β  Β  Β  Β  Β  Handouts πŸ”— ;Β  Exercise sheet πŸ”— ;Β  Computer practicals πŸ”—
Likelihood methods & math stats: Handouts πŸ”— ;Β  Exercise sheet πŸ”—

Undergraduate level IV

Spatio-temporal statistics IV (MATH4341)
Michaelmas
(point referenced data analysis; areal data analysis; point pattern data analysis; INLA)
Description πŸ”—Β  ;Β  Handouts πŸ”— ;Β  Exercise sheet πŸ”— ;Β  Problem classes πŸ”—

Undergraduate level III

Machine learning and neural networks III (MATH3431)
Epiphany
(stochastic gradient, support vector machines, kernelised models, GP, neural networks)
Description πŸ”— ; Handouts πŸ”— ; Exercise sheet πŸ”— ; Computer practicals πŸ”—
Description πŸ”—Β  ;Β  Handouts πŸ”— ;Β  Exercise sheet πŸ”— ;Β  Computer practicals πŸ”—

Statistical methods III (MATH3051)Β 
Epiphany
(linear models, PCA)
  • Teaching material available from DUO
    2022
Handout for Mixtures & EM πŸ”— ; Code for PCA on images πŸ”—

Undergraduate level I

Statistics (MATH1541)
Epiphany
  • Teaching material available from DUO
    2017, 2020



Short courses ...


Introduction to Bayesian Statistics (UTOPIAE)
Β  Summer, 2018
  • @Durham Training School, Department of Mathematical sciences, Durham University, UK
  • Learning material in GitHub πŸ”— ; Handouts πŸ”— ; Slides πŸ”—
  • Karagiannis G.P. (2022) Introduction to Bayesian Statistical Inference. In: Aslett L.J.M., Coolen F.P.A., De Bock J. (eds) Uncertainty in Engineering. SpringerBriefs in Statistics. Springer, Cham.


Introduction to Gaussian process regression (SURF)

August, 2016


Markov chain Monte Carlo (in course: Introduction to Uncertainty Quantification)

March, 2016



Web applets for the courses...


Web applets are available from : GitHubΒ  πŸ”—



[AppAnywhere πŸ”—]Β  ; [DUO πŸ”—]Β  ; [Blackboard ULTRA πŸ”—]