COMP3354 Statistical Learning [Section 1A, 2018]
The challenges in learning from big and complicated data have led to significant advancements in the statistical sciences. This course introduces supervised and unsupervised learning, with emphases on the theoretical underpinnings and on applications in the statistical programming environment R. Topics include linear methods for regression and classification, model selection, model averaging, basic expansions and regularization, kernel smoothing methods, additive models and tree-based methods. We will also provide an overview of neural networks and random forests.
On successful completion of the course, students should be able to:
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[1] demonstrate an understanding of the conceptual underpinnings of various statistical learning techniques in terms of how, why and when each method works in different real-life scenarios [2] critically evaluate the analytical strategies adopted in applying statistical learning techniques to different areas [3] apply basic statistical learning methods to perform exploratory data analysis and build predictive models using the statistical programming environment R, with real datasets [4] properly tune, select, and validate statistical learning models [5] interpret the results and discuss their implication |