Course Resources for ML Course at IBA for Masters and PhD Students (Dec 2020-Jan 2021, 4 Lectures)
Welcome All,
This repository contains the course outline, 4 lecture slides and in due time, 4 R code files will be added as well. There will be one example per lecture to illustrate a basic computational implementation of each of the ML methods covered in total of four lectures.
The topic for lecture 1 is introduction to machine learning (ML), relationship between ML and econometrics and shrinkage estimators such as LASSO and Ridge Regression.
In Lecture 2, we will cover multi-armed bandit problems and two common algoirthms to solve them which are Upper Confidence Bound Algorithm (UCB) and Thompson Sampling Algorithm.
Deep Neural Networks will be covered in Lecture 3.
Computational Linguistics, especially Latent Dirichlet Allocation (LDA) will be our topic in Lecture 4.
In each lecture, I will discuss economic applications of these methods from recent economic literature and I will briefly discuss how to implement these methods in R.
Thanks, Sonan Memon (Lecturer at IBA Karachi in Economics Department).