Course Information

Lecturer:

Dr Lamiae Azizi, University of Sydney

Synopsis:

Statistical machine learning merges statistics with the computational sciences—computer science, systems science and optimization. Much of the work in statistical machine learning is driven by applied problems in science and technology, where data streams are increasingly large-scale, dynamic and heterogeneous, and where mathematical and algorithmic creativity is required to bring statistical methodology to bear.

In this course we will study how to use probability models to analyze data, focusing on the mathematical details of the models and the algorithms for computing them. We will study both foundations and advanced methods. The goal of the course is to understand modern probabilistic modelling, and develop good practices for specifying and applying probabilistic models to analyze real-world data. The applications we will use range from customer purchases in on-line stores, the modelling of price changes in financial markets, the analysis of the connectivity of genes in biological systems, the discovery of new materials with optimal properties and the design of more efficient hardware.

Course Overview:

  • Foundations of graphical models
  • Bayesian networks
  • Bayesian nonparametrics:
    • Dirichlet processes
    • Gaussian processes
  • Algorithmic inference and sampling methods
  • Scalable inference
  • Stochastic variational inference

3 Contact hours

28 hours

Prerequisites:

  • Multivariable calculus.
  • Second year probability and statistics.
  • Familiarity with a programming language for statistics, such as R or Python.

Assessment:

  • Mid-school assignment: 40%
  • Final examination: 60%

Resources:

  • Jordan, M. (2003). An Introduction to Probabilistic Graphical Models.
  • Koller, D. and Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
  • J. Ghosh and R. Ramamoorthi. Bayesian Nonparametrics. Springer, 2003
  • R. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto, 1993.

Lecturer Biography

Statistical machine learning

Dr Lamiae Azizi, University of Sydney

Dr Lamiae Azizi is a Lecturer in the School of Mathematics and Statistics at Sydney University. She received a PhD in Applied Mathematics from Joseph Fourier University (France) and after two years at Cambridge (UK), she moved to Australia as an NHMRC Research Fellow in the University of Sydney Medical School and then as a lecturer in the School of Mathematics and Statistics. Her research interests are developing Bayesian statistical machine learning models for real life applications. More information is available at http://sydney.edu.au/science/people/lamiae.azizi.php

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