# Statistical Models and Methods for Business Analytics

IDS 575 (Spring 2019)

Document version: Jan 27 2019

## Overview

The goal of this class is to cover the foundations of modern statistics and machine learning complementing the data mining focus of IDS 572. In other words, you will get up to speed with the requisite background as well as the key theoretical underpinnings of modern analytics. We will do so through the lens of statistical machine learning.

## Previous Editions

- Spring 2018 (has videos!)
- Fall 2017 (has videos!)

## Logistics

- Lectures: Mondays 6.00 PM to 8.30 PM at BH 208
- Optional Recitations: Thursdays 11 AM to 12 noon at BH 309
- Staff
- Instructor: Dr. Theja Tulabandhula
- Teaching Assistant: Parshan Pakiman

- Online communication: Forum (sign up needed!)
- Offline communication:
- Instructor Office Hours: Wednesdays 3.00 PM to 4.30 PM at UH 2404
- TA Office Hours: Wednesdays 5 PM to 7 PM at UH2432

## Textbook and Materials

- Textbook I: Elements of Statistical Learning II.
- Textbook II: An Introduction to Statistical Learning with Applications in R.
- Refresher on probability
- Refresher on linear algebra

## Software

- The R programming language and the RStudio IDE.

## Schedule (*tentative*)

#### 01/14 : Supervised Learning: Linear Models and Least Squares, k-Nearest Neighbor Methods

#### 01/28 : Towards Regression: Statistical Decision Theory, Curse of Dimensionality, Linear Regression, Categorical Variables, Interaction Terms

#### 02/04 : Regression I: Bias-variance Trade-off, Subset Selection, Cross-Validation

#### 02/11 : Regression II: Ridge Regression, LASSO (Least Absolute Shrinkage and Selection Operator)

- Lecture note
- Machine Learning Mindmap
- Linear regression by gradient descent
- Handwritten note
- Lecture video 1
- Lecture video 2

#### 02/18 : Classification: Linear Discriminant Analysis, Logistic Regression, Model Assessment and Selection: AIC, BIC and Validation

#### 02/25 : The Bootstrap, Maximum Likelihood Estimation and Review of Linear Models

#### 03/11 : Expectation Maximization and Sampling (Markov Chain Monte Carlo)

#### 03/18 : Applications of regression, classification and likelihood maximization

- Guest lecture by Parshan Pakiman
- Lecture note

#### 04/01 : Tree Methods, Adaboost and Gradient Boosting

#### 04/08 : Random Forests, Multivariate Adaptive Regression Splines and Support Vector Machines

#### 04/15 : Kernel Trick, Introduction to Unsupervised Learning, Association Rules

#### 04/22 : Unsupervised Learning: Clustering, Principal Component Analysis and Spectral Clustering

#### 04/29 : Time Series and Supervised Learning, and the ARMA Model

## Assignments

- 01/28: Assignment 1 out. Due on 02/10
- 02/11: Assignment 2 out. Due on 02/24
- 02/25: Assignment 3 out. Due on 03/17
- 04/01: Assignment 4 out. Due on 04/14
- 04/15: Assignment 5 out. Due on 04/28

These involve reimplementing statistical techniques and understanding their behavior on interesting datasets. Always mention sources in your assignment solutions. Submission deadline is BEFORE 11.59 PM on the concerned day. Late submissions will have an automatic 20% penalty per day. Use Blackboard for uploads.

## Exams

- 03/04: Exam I (same venue as lectures, and during class hours)
- 05/06: Exam II (same venue as lectures, and during class hours)

These are closed book, but one 8.5x11-inch handwritten cheatsheet is allowed. No computers and communication devices are allowed.

## Grades

- Assignments: 8% + 8% + 8% + 8% + 8%
- Exams: 22% (Exam I) + 30% (Exam II)
- Participation: 8% (online and offline)

## Miscellaneous Information

- This is a 4 credit graduate level course offered by the Information and Decision Sciences department at UIC.
- Please see the academic calendar for the semester timeline.
- Students who wish to observe their religious holidays (http://oae.uic.edu/religious-calendar/) should notify the instructor within one week of the first lecture date.
- Please contact the instructor at the earliest, if you require accommodations for access to and/or participation in this course.
- Please refer to the academic integrity guidelines set by the university.