MACHINE LEARNING IN MANAGEMENT

Teaching in italian
MACHINE LEARNING IN MANAGEMENT
Teaching
MACHINE LEARNING IN MANAGEMENT
Subject area
SECS-S/06
Reference degree course
DIGITAL MANAGEMENT
Course type
Bachelor's Degree
Credits
6.0
Teaching hours
Frontal Hours: 36.0
Academic year
2022/2023
Year taught
Course year
3
Language
ENGLISH
Curriculum
ECONOMICO

Teaching description

Teaching program is provisional and may be subject to changes

Basic elements of calculus and statistics for data analysis.

Introduction to machine learning (supervised and unsupervised learning). All the presented tools are illustrated in several real case studies with the software R.

Knowledge and understanding:

Knowledge and understanding of machine learning algorithms, including supervised and unsupervised methods.

 

Applying knowledge and understanding:

Ability to extract relevant information from big dataset for management and business innovation.

Ability to identify the machine learning models that are suitable to correctly analyse a specific business problem.

Ability to use a specific programming language to implement machine learning procedures.

 

Making judgments:

Making judgements on the output of a machine learning methods also by analyzing its pros and cons.

 

Communication skills:

To present in a concise way the results of a quantitative analysis with machine learning algorithm and to discuss its implications in management.

 

Learning skills:

Ability to formalize in an algorithmic form a problem of interest in management.

Frontal lectures, exercises, computer labs.

The exam consists of two parts:

  • a written exam with review questions and short exercises, including questions about R coding and output interpretation.
  • preparation and presentation of a quantitative analysis with machine learning methods (i.e., project work) with the help of the software R.

To pass the exam students must obtain a positive evaluation on both the project and the written exam. Both parts weigh 50% of the total points.

 

There is no difference in the assessment procedures between attending and non-attending students.

 

University of Salento “promuove e garantisce l’inclusione e la partecipazione effettive degli studenti con disabilità” (art. 10 of the Statute). Students that have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) could contact the Disability and Accessibility Offices in Student Services: paola.martino@unisalento.it

More information will be available on the course webpage at elearning.unisalento.it

Introduction to Data Science and Machine Learning.

Unsupervised learning. K-means algorithms.

Supervised learning: linear regression. Ridge and Lasso regression.

K-Nearest neighbour algorithms.

Supervised learning: classification. Logistic regression.

Decision trees.

Lectures notes will be provided. The teaching material will be made available through the Lecture webpage at elearning.unisalento.it.

 

Suggested reading:

· Boehmke, B. and Greenwell, B.: Hands-on Machine Learning with R. Free available at

https://bradleyboehmke.github.io/HOML/

· Hull, J.C.: Machine Learning in Business – An introduction to the world of data science, 2021. Third edition. Slides avalilable at https://www-2.rotman.utoronto.ca/~hull/

James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R. Springer, 2022. Second edition. Free available at https://www.statlearning.com/

Semester

Exam type
Compulsory

Type of assessment
Oral - Final grade

Course timetable
https://easyroom.unisalento.it/Orario

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