This course will expose students to the most popular forecasting techniques used in industry. This course aims to prepare students to develop product solutions that deliver user value and provide viability for the business in the technology space that is heavily using Machine Learning. Models with lagged variables are considered, as is estimation with instrumental variables, two-stage least squares, models with limited dependent variables, and basic time-series techniques. The course begins with a discussion of the linear regression model and examination of common problems encountered when applying this approach, including serial correlation, heteroscedasticity, and multicollinearity. This course focuses on the application of statistical tools used to estimate economic relationships. Industries covered will vary but may include the Financial Industry, Healthcare, Manufacturing, Defense, and Biotech for illustrative examples.Įlectives (Choose at least three courses) We will start with the intent of governance, its roots, its current manifestations and identify trends that are shaping algorithmic decision-making governance with a focus on for-profit firms, mainly the US. The recent acceleration in the use of Artificial Intelligence (AI) and specifically Machine Learning (ML) techniques have introduced unique opportunities and risks that require governance to encourage their responsible and ethical use. This is a survey course of governance frameworks & techniques for algorithms that are used to make decisions within an organization or in servicing clients. Prerequisites: Data analysis and feature engineering, traditional machine learning theory and practice, python programming (intermediate level – e.g., familiarity with sci-kit learn, matplotlib, NumPy, pandas), linear algebra, and first-order derivatives.Īlgorithmic Ethics and Governance – from traditional to AI/ML The course uses Python as the programming language. The course provides a high-level theoretical overview of each section and discusses practical applications through hands-on projects. This course aims to teach students advanced AI algorithms and covers neural networks, deep learning architectures, and reinforcement learning. No prior experience with R or Python is necessary. Students will apply both supervised and unsupervised machine learning techniques to solve various economics-related problems with real-world data sets. The main topics covered in this course include: advanced regression techniques, resampling methods, model selection and regularization, classification models (logistic regression, Naive Bayes, discriminant analysis, k-nearest neighbors, neural networks), tree-based methods, support vector machines, and unsupervised learning (principal components analysis and clustering). This course demonstrates how to merge economic data analysis and applied econometric tools with the most common machine learning techniques, as the rapid advancement of computational methods provides unprecedented opportunities for understanding “big data.” This course will provide a hands-on experience with the terminology, technology and methodologies behind machine learning with economic applications in marketing, finance, healthcare, and other areas. This course aims to prepare students to understand the data engineering required for data science research projects and industry products.
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