An introduction to science, technology and business

An introduction to science, technology and business

InstructorRoberto Battiti
PeriodSecond semester
Length (h)6

Course Objectives

Like most businesses, tourism and hospitality are undergoing a phase of disruptive innovation caused by the wider adoption of computers, networks and, above all, sophisticated and powerful algorithms. Algorithms automate business processes in a partial or total manner, by starting from repetitive and simple tasks but progressively reaching also more complex and “creative” tasks, traditionally associated with human decision making. The concepts of “automated creativity” or “automated business innovation” sound like contradictions. We like to think that only human people can discover truly innovative ways of solving problems and radically improving business performance. In this course we summarize two theoretical advancements in the past years which permit this disruptive innovation: machine learning and intelligent optimization.

Managers and decision makers reach decision by some level of anticipation (expectation, prediction) of the effects of different choices. These decisions are based on a series of “What if?” questions, with answers given by expertise, gut feeling, or some level of logical and mathematical modelling. Machine learning or learning from data is a theory for deriving flexible models by starting only from the data produced by the business. After a model is available, computers can simulate the effects of zillions of possible decisions, by predicting the output (for example the total profit of the hotel), and by creating and selecting one among the best decisions. Intelligent optimization is this automated process of creating in an intelligent manner a large series of possible decisions, aiming at improving the current way of doing business.

Through machine learning and intelligent, optimization hotel managers have extremely powerful tools in their pockets to improve total profitability and customer satisfaction. It is up to them to understand the new possibilities (the overall vision), decide which possible changes they are considering (e.g., acting on prices, availability of different types, reservation rules, kind of offer, etc.), collect and organize the relevant data about the past performance, deliver them to ML tools to build models and run zillions of software experiments via intelligent optimization (IO) to identify improving solutions.

In this course we will highlight some fundamental tools and use simulation-based optimization software for exercises with the participants about managing a test hotel and measuring the improvement in profitability that can be obtained by LION techniques in realistic contexts.

Course Content Summary

  • Machine learning and optimization
  • Demand forecasting and opinion mining
  • Revenue management
  • Recommender systems

Teaching methods

Lectures and teamwork


The LION way. Machine Learning plus Intelligent Optimization.
R. Battiti and M. Brunato
LIONlab, University of Trento, Italy, Version 3.0, Apr 2017.