Machine Learning for modelling and predicting economic and financial data (R)

Machine Learning for modelling and predicting economic and financial data (R)

Machine Learning for modelling and predicting economic and financial data (R)

Instructors : Prof. Monday ADENOMON & Timothy A. OGUNLEYE

July 14, 2023 - July 15, 2023

This conference is currently not open for registrations or submissions.

About the Short Course

This short course provides a general introduction to supervised machine learning with a particular focus on classification and regression models. It aims at providing an overview of more robust skills in using R syntax to handle various computations relating to machine learning models. Here, the use of R will be described using some packages like caret, Rpart plot, etc. Models such as logistic regression, support vector machine, Decision tree, Random Forest and Neural Network would be introduced with Hands-on practical using real life data in economics and financial stocks. Lastly, the processing of tuning machine learning to improve model’s performance will be treated.

In-Person Event. Location Of Short Courses: University of Ottawa


Who is this course for?

This training is specifically targeted at both graduate and undergraduate students in the fields of data science, computer science, statistics, mathematics, economics, finance, econometrics, management sciences, engineering, etc. Academics, Independent Researchers, Bank Officials, Staff of Ministry, Department and Agencies of Government Users of machine learning in economics and finance.

Level Of Instruction: Intermediate


Course details

Day - 1

• Introduction of machine learning with interest in economic and finance 
• R for machine learning using caret R package
• Classification in financial stocks using logistic regression
• Practical Discussions, Trainee hands-on on personal project "

 Day - 2

• Classification and Regression using support vector machine approach
• Classification and regression using decision tree 
• Classification and regression using Neural Network


Learning Outcomes

At the end of this course, participants would understand the following:

• Application of machine learning in economics and finance
• The potentials of caret package in R for machine learning
• Classification and regression using logistic, support vector machine, Decision tree, Random forest and Neural network.
• Techniques in improving the performance of machine learning models

Course Materials

This short course hopes to cover the following areas:

• Introduction of machine learning with interest in economic and finance
• R for machine learning using caret R package
• Classification in financial stocks using logistic regression
• Classification and regression using support vector machine approach
• Classification and regression using decision tree
• Classification and regression using Random Forest
• Classification and regression using Neural Network.

Delivery Structure

Physical lectures involving classroom discussion Power point presentations, Flip chart stand, paper and markers Syndicate exercises and/or case studies (practical)

Knowledge Assumed:

Anyone attending this short course should have at least 25% knowledge about the use of R programming as the case may be. That is little or average knowledge of R programming.

Preparatory Material:

• R or R studio (Posit) software
• Sample Data (To be release by the instructors)
• Lecture Slides
• 2 to 3 minutes video of the instructors explaining other required materials.


About the instructor:  Monday Osagie Adenomon

Professional position: Senior Lecturer in Statistics, Department of Statistics, Nasarawa State University, Keffi, Nigeria (since 2019).Training and education: PhD (Statistics)2016; M.Sc.(Statistics) 2010; B.Sc. (Statistics) 2021; PGDS (Statistics) 2008; HND (statistics) 2004. Research interests: Econometrics; Time series Analysis; Financial time series analysis; Spatial Econometrics and Interdisciplinary Statistical Analysis. Professional services: Newly Elected Member ISI (2022 till date); ISI Short Course and Outreach Officer (2021-2023); Chair of the International Association of Statistical Computing (IASC) African Members Group (2021 till date); Team Lead, Ambitious Africa Nigeria team (2020 till Date); Lead-Organizer of the Northern Nigeria LISA 2020 Symposium (2020); Lead-Organizer, IASC physical symposium in Nigeria (2019 and 2021);Member, Committee of Sport Statistics Research Group of International Statistical Institute (ISI) (2019 till date); Chartered Statistician of the Royal Statistical Society (2019 till Date); Coordinator, NSUK-LISA Stat Lab (2018 till Date); Founder, Foundation of Laboratory for Econometrics & Applied Statistics of Nigeria (aka Found-LEASin-Nigeria) (2018 till Date); Mentor, LISA 2020 Networks (2018 till date) and Advisers to more than ten (10) PhD candidates in Statistics.

Affiliations: Department of Statistics, Nasarawa State University, Keffi, Nigeria

About the instructor: Timothy A. Ogunleye

Presently, Timothy A. OGUNLEYE holds two degrees in Statistics: Master of Science (M.Sc.) and Bachelor of Science (B.Sc. – Hons.) from one of the prestigious universities in Nigeria – University of Ilorin. This is in addition to ordinary National Diploma and Higher National Diploma in Statistics obtained from the Federal Polytechnic, Ede, Osun State, Nigeria. He has more than 15 years of work experiences that cut across both industries and academia. Timothy has worked for a number of local and international organizations including UNDP, NAPTIP, NAICOM, LBS, to mention but a few. He’s also an experienced academic university staff. Currently, he’s a lecturer at the Department of Statistics, Osun State University, Osogbo, Nigeria. He is acting as the Secretary General, International Association for Statistical Computing - African Members Group. His research areas includes modelling and econometrics, computational statistics, morphometric and biostatistics.

Affiliations: Department of Statistics, Osun State University, Osogbo, Nigeria

This conference is currently not open for registrations or submissions.