Overview
Machine learning is a relatively new approach to data analytics, which places itself in the intersection between statistics, computer science, and artificial intelligence. Its primary objective is that of turning information into knowledge and value by “letting the data speak”. To this purpose, machine learning limits prior assumptions on data structure, and relies on a model-free philosophy supporting algorithm development, computational procedures and analytical solutions. Computationally, machine learning was unfeasible a few years ago, it is a product of the computerised era, of today's machines' computing power and ability to learn. It is also a product of hardware development and continuous software upgrading. This course is a primer to machine learning techniques in Eviews.
The latest edition of Eviews 11 offers various packages to perform machine learning. After the course, participants are expected to have an improved understanding of EViews' potential to perform some of the most used machine learning techniques, thus becoming able to master research tasks and specifically to master model selection techniques.
Agenda
Day 1
Session 1
- Machine Learning: definition, rational, usefulness
- Supervised vs. unsupervised learning
- Regression vs. classification problems
- Inference vs. prediction
- Sampling vs. specification error
- Goodness-of-fit measures
- Measuring the quality of fit: in-sample vs. out-of-sample prediction power
- The bias-variance trade-off and the Mean Square Error (MSE) minimization
- Training vs. test mean square error
- The information criteria approach
Session 2: Model selection as a correct specification procedure. The Elastic Net regression in Eviews
Day 2
Session 1:Ridge Regression in Eviews: improving on the forecasting performance of the OLS estimator
Session 2: Lasso Models in EViews : how to avoid model misspecification and over fitting while extracting all the relevant information . How to set the lass0 penalty parameter and interpreting the results. Post estimation diagnostic checkes.
Session 3: Q&A session with instructor
Prerequisites
Basic knowledge of linear regression is helpful but not necessary. An introductory level of Eviews is helpful but not required.
- Cost includes course materials.
- Delegates are provided with temporary licences for the software(s) used in the course and will be instructed to download and install the software prior to the start of the course.
- Payment of course fees required prior to the course start date.
- Registration closes 1-calendar days prior to the start of the course.
- 100% fee returned for cancellations made over 28-calendar days prior to start of the course.
- 50% fee returned for cancellations made 14-calendar days prior to the start of the course.
- No fee returned for cancellations made less than 14-calendar days prior to the start of the course.
The number of delegates is restricted. Please register early to guarantee your place.