COMP9418 (Advanced Topics in Statistical Machine Learning) - UNSW Sydney
The course has a primary focus on probabilistic machine learning methods, covering the topics of exact and approximate inference in directed and undirected probabilistic graphical models - continuous latent variable models, structured prediction models, and non-parametric models based on Gaussian processes.
There was major emphasis on maintaining a good balance between theory and practice, and my primary responsibility was creating lab exercises to help students gain hands-on experience in applying these methods on real-world data with the current tools and libraries. The labs were purely implemented in Python, and relied heavily on the Python scientific computing and data analysis stack (NumPy, SciPy, Matplotlib, Seaborn, Pandas, IPython/Jupyter notebooks), and the popular machine learning libraries scikit-learn and TensorFlow.
Students were given the chance to experiment with a broad range of methods on various problems, such as Markov chain Monte Carlo (MCMC) for Bayesian logistic regression, probabilistic PCA (PPCA), factor analysis (FA) and independent component analysis (ICA) for dimensionality reduction, hidden Markov models (HMMs) for speech recognition, conditional random fields (CRFs) for named-entity recognition, and Gaussian processes (GPs) for regression and classification.