Business Statistics 41000 - Booth MBA

Class Notes

• Section 1: Introduction and Probability (code)

• Section 2: Statistics and Data (sec2.R, epl-mu.r, abtest.R)

• Section 3: Regression (utils.R, housing.r, mammals.R)

• Section 4: Classification (Tree Notes newfood.R, oj.R, pga.R, Tennis Notebook, logistic.r)

• Section 5: AI and Deep Learning. Examples: MNIST; NN for Circle Data; IMDB: (notebook, code); Trump Tweets (notebook, code)

HW

• HW1 (Rscript)

• HW2 (Rscript)

• HW3 (Rscript)

• HW4 (Rscript)

• Final Project (Rscript)

Midterm

• Bayes Questions

• True/False Questions

• Midterm 1

• Midterm 2

• Midterm 3

• Midterm 4

• Midterm 5

• Midterm 6

• Midterm 7

• Midterm 8

• Midterm 9

• Midterm 10

• Midterm 11

R

I made a quick introduciton to R, video is here and here are the slides.
To get RStudio on you computer, you need to first download and install R then you need to download and install RStudio. Further, I recommend doing those two courses on DataCamp.

• Introduction to R

• Introduction to Data
  
  

In addition, Booth offers access to an online R tutorial on LinkedinLearning. To access the training, go to: here and login using your CNetID and password. You may find it helpful to work through the following R courses offered on LinkedInLearning:

• Code Clinic: R with Mark Niemann-Ross

• R Statistics Essential Training with Barton Poulson

• Up and Running with R with Barton Poulson

Demos

• Portfolio Variance

• Binomial

• Normal Distribution

Other Materials and Links

• AI in Tennis

• Retail: Kraft-Heinz blog, Kraft-Heinz WSJ, Frito-Lay potato peeling, Tyson, Anheuser-Busch InBev payments, Walmart smart store

• The State of Data Science and Machine Learning

• Model Interpretability

• Data Sceince Survey

• J. M. Keynes's Investment Performance: A Note

• Bayes Search

• How Y'all (NY Times)

• Monte Hall

• EPL

• Data Cleaning

• Airbnb (Random Forest)

• Facebook (Decision trees and logistic regrsssion)

• Youtube (deep learning)

• Uber (time series)

• Exploratory Data Analysis

• Overfitting

• Election 2016

Deep Learning

• Course by Jeremy Howard on coding aspects of deep learning

• Short introduction into deep learning

• Architectures

• Keras

• ConvNetJS

• Opt Visualization

• Momentum

• Backprop