MACHINE LEARNING AND BIG DATA ANALYSIS
- Scheda dell'insegnamento
- Obiettivi formativi
- Metodi didattici
- Verifica dell'apprendimento
- Altre informazioni
Machine learning (ML) is a branch of Artificial Intelligence (AI) that was originally developed to enable computers to emulate human cognition and learn from training examples to predict future events. Today, ML techniques include a number of advanced statistical methods for regression and classification applied in a wide variety of fields (including medical diagnostics, credit card fraud detection, face and speech recognition and analysis of the stock market) where the main goal is to directly predict the dependent variable of interest, without focusing on the underlying relationships between the explanatory variables.
The statistical methods developed in the ML literature (also known as Statistical Learning methods) have been particularly successful in “Big Data” settings, where we have either information on a large number of units, or many pieces of information on each unit (or both).
The aim of this course is to present Machine Learning Techniques using an econometric perspective.
In particular, during this course, students will learn the various concepts and techniques intensively used in the Machine Learning literature such as random trees, random forests, boosting, neural networks and deep learning, and their natural extensions to time series analysis and causal inference, with the complement of many practical examples.
By the end of this course students are expected to be able to master and implement most of these techniques on real data problems using the statistical software R.
Intermediate knowledge of econometrics, statistics and linear algebra.
The course is organized in 10 lectures (of about 4h each) and will cover the following topics.
1 Introduction, basic concepts and definitions. History and Foundations of Econometric and Machine Learning Models (4 hours).
2 Statistical Learning: Supervised Versus Unsupervised Learning, Regression Versus Classification Problems. Assessing Model Accuracy: Quality of Fit measures and the Bias-Variance Trade-Off. Introduction to R (4 hours).
3 Linear Regression Models: Refresh, Extensions and Potential Problems. Examples of Linear Methods for Regression in R (4 hours).
4 Classification: Linear Methods, Logistic Regression, Linear Discriminant Analysis. Comparison of Classification Methods in R (4 hours).
5 Model Validation and Selection: Cross-Validation and Bootstrap techniques. Shrinkage Methods: Ridge Regression and LASSO. Dimension Reduction Methods: PCA and PLS (4 hours).
6 Non-linear models: Polynomial Regression, Regression and Smoothing Splines, Local Kernel Estimation, Generalized Additive Models. Examples: Non-linear Modeling with R (4 hours).
7 Tree-Based Methods: Regression and Classification Trees, Bagging, Random Forests, Boosting. Lab: Decision Trees in R. (4 hours).
8 Neural Networks and Deep Learning: Model selection and examples with R (4 hours).
9 Support Vector Machines and Flexible Discriminants: Comparison with Logistic Regression and Practical Examples (4 hours).
10 Extension of Machine Learning Techniques to Time Series and Causal Inference. Group “Hands-On Regression and Classification” using real data (4 hours).
Frontal Lectures in class coupled with practical examples and group “Hands-On” sessions with real data applications.
The final assessment will be based (50%) on a written exam (the same for both attending and non-attending students) composed by 4 questions (to be answered in 2h) and (50%) on a take-home exercise in which the students will be asked to apply on real data examples (like a sort of kaggle-type competition) the techniques learned during the lectures.
No partial exams will be held for this course.
• James G., Witten D., Hastie T., Tibshirani R. (2014) An Introduction to Statistical Learning: with Applications in R, Springer Publishing Company, Incorporated, (updated version 2017)
• Hastie T., Tibshirani R., Friedman J. H. (2009). The elements of statistical learning: data mining, inference, and prediction. 2nd ed. New York: Springer (selected parts).
• Slides and additional readings that will be provided during the course at the end of each lecture and uploaded on the e-learning platform.
The syllabus can be subject to modifications and changes during the course. Please check periodically the course page on e-learning for possible changes and communications by the instructor.
Office hours for students:
Usually on Thursday, from 16.30 to 18.30.
Department of Economics, Monte Generoso (MTG) building, first floor, room 25). Tel. 0332 39 5544
For organizational reasons, please send an email in advance to the instructor, in order to schedule a meeting with him (both within and outside the “official” office hours).
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