## Courses

##### Last Updated:

28/08/2017 - 21:10

## IAM557 - Statistical Learning and Simulation

**Credit: **
3(3-0); **ECTS: **
8.0

**Instructor(s): **
Ömür Uğur

**Prerequisites: **
Consent of Instructor(s)

#### Course Catalogue Description

Brief introduction to Statistical Learning: Regression versus Classification; Linear Regression: simple and multiple Linear Regression; Classification: Logistic Regression, Discriminant Analysis; Resampling Methods: Cross-Validation, the Bootstrap; Regularization: Subset Selection, Ridge Regression, the Lasso, Principle Components and Partial Least Squares Regression; Nonlinear Models: Polynomial; Splines; Generalized Additive Models; Tree-Based Models: Decision Trees, Random Forest, Boosting; Support Vector Machines; Unsupervised Learning: Principle Component Analysis, Clustering Methods.#### Course Objectives

At the end of the course, the student will learn:- the fundamentals of Statistical Learning, regression and classification
- linear and nonlinear regressions including splines
- Generalised Additive Models for both regression and classification problems
- regularisation techniques including Ridge regression and the Lasso
- the tree-based methods for regression and classification
- Support Vector Machine which is highly appreciated among Data Science and Machine Learning Community
- the difference between supervised and unsupervised learning methods

#### Course Learning Outcomes

Student, who passed the course satisfactorily will be able to:- present the data and its descriptive analysis
- distinguish between regression and classification problems
- apply regression or classification algorithms to solve related problems
- code their own algorithms for specific applications in Statistical and Machine Learning
- understand the fundamentals of Support Vector Machine and be able to apply to specific problems
- distinguish between supervised and unsupervised learning methods in related applications

#### Tentative (Weekly) Outline

- Brief introduction to Statistical Learning: a) Regression versus Classification
- Linear Regression: a) simple and multiple Linear Regression
- Classification: a) Logistic Regression b) Discriminant Analysis (Linear and Quadratic)
- Resampling Methods: a) Cross-Validation b) the Bootstrap
- Regularisation: a) Subset Selection b) Ridge Regression c) the Lasso d) Principle Components Regression e) Partial Least Squares Regression
- Nonlinear Models: a) Polynomial and Splines b) Generalised Additive Models
- Tree-Based Models: a) Decision Trees b) Random Forest c) Boosting
- Support Vector Machines
- Unsupervised Learning: a) Principle Component Analysis b) Clustering Methods

#### Course Textbook(s)

- Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, An Introduction to Statistical Learning - with Applications in R, 8th ed., Springer, 2013 (Corrected at 8th printing 2017)

#### Supplementary Materials and Resources

- Books:
- Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd ed., Springer, 2009 (Corrected at 12th printing 2017)
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, 2012
- Peter Harrington, Machine Learning in Action, Manning Publications Co., 2012
- Charu C. Aggarwal, Neural Networks and Deep Learning: A Textbook, Springer, 2018
- G. Jay Kerns, Introduction to Probability and Statistics Using R, 1st ed., 2015
- Robert V. Hogg, Elliot A. Tanis, Dale Zimmerman, Probability and Statistical Inference, 9th ed., 2015
- Larry Wasserman, All of Statistics - A Concise Course in Statistical Inference, 2004
- W. N. Venables, D. M. Smith, and the R Core Team, An Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics, Version 3.4.2 (2017-09-28)
- Resources:
- The R Project for Statistical Computing: https://www.r-project.org/
- python: https://www.python.org/
- RStudio: https://www.rstudio.com/
- Anaconda: https://www.anaconda.com/

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