"The core topics are cross-validation, shrinkage methods (ridge regression, the LASSO, etc.), neural networks, gradient boosting, separating hyperplanes, support vector machines, basis expansion and regularization (e.g., smoothing splines, wavelet smoothing, kernel smoothing), generalized additive models, bump hunting, multivariate adaptive regression splines (MARS), self-organizing maps, mixture model-based clustering, ensemble learning, and p>>n problems. For computing, the R software will be used as well as either Julia or Python."
"Multivariate distributions: Normal, Wishart, T2 and others; regression, correlation, factor analysis, general linear hypothesis."
"Maximum Likelihood Estimation, Cramer-Rao bound, Likelihood Ratio tests, Multi-parameter likelihood methods, Sufficient Statistics, Completeness and MVUE, Exponential Family, Functions of parameters, Uniformly Most Powerful Tests, Generalized likelihood ratio tests, Quadratic forms, Analysis of variance, Introduction to Bayesian Inference"
"general linear model. Applied regression analysis. Incomplete block designs, intra- and inter-block analysis, factorial designs. Random and mixed models. Distribution theory, hypothesis testing, computational techniques."
"Stationary, auto-regressive and moving-average series, Box-Jenkins methods, trend and seasonal effects, tests for white noise, estimation and forecasting methods, introduction to time series in the frequency domain."
"multivariate latent variable models which assume low dimensional latent variable structures for the data. Multivariate statistical methods including Principal Component Analysis (PCA), and Partial Least Squares (PLS) are used for the efficient extraction of information from large databases typically collected by on-line process computers. These models are used for the analysis of process problems, for on-line process monitoring, and for process improvement"
"Searching, optimization, online search agents. Constraint satisfaction. Knowledge, Reasoning and Planning: Logic and Inference, Planning and Acting, Knowledge Representation. Knowledge and Reasoning with Uncertainty. Machine learning problems, training and testing, overfitting. Modelling strategies: data preprocessing, overfitting and model tuning. Measuring predictor importance. Factors that Can Affect Model Performance. Feature selection. Measuring performance of classification models."
From the Contents on: https://academiccalendars.romcmaster.ca/content.php?catoid=39&navoid=8149
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