Decision making is difficult. During the course, we will talk about different angles of the decision-making process starting from organizational context, experimental design, the requirement to the data that are used to derive analytics, statistical concepts used for the hypothesis testing and how experiments and hypotheses derive management decisions.
In addition, we will discover how to experiment with design principles that influence the feasibility of AI/ML solutions.
This course will walk students through different angles of the decision-making process. It is built to exercise uncertainty and gives instruments to fight it.
Students should be able to:
⦁ basic statistics knowledge
⦁ basic proficiency in python 3
⦁ logical reasoning
⦁ communicating ideas and solutions to a practical problem in a written and oral form
At the end of this course, the student should be able to:
⦁ Design real-life experiments
⦁ Analyze if experiment outcomes are statistically significant
⦁ Use decision-making frameworks
⦁ Analyze And Understand the business context of the AI/ML projects
⦁ Know and recognize potential issues with production AI/ML projects