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Introduces linear regression, covering line fitting, training, gradients, and multivariate functions, with practical examples like face completion and age prediction.
Introduces the fundamentals of regression in machine learning, covering course logistics, key concepts, and the importance of loss functions in model evaluation.
Explores the stochastic properties and modelling of time series, covering autocovariance, stationarity, spectral density, estimation, forecasting, ARCH models, and multivariate modelling.
