MBA 2435 Strategic Insight from Machine Learning
Digitalization creates high speed, high volume digital activity traces – Big Data. Using recent advances in machine learning, organizations can use these data to build models that predict specific events in real-time. Managers can use machine learning-based analytics to improve a wide variety of tasks ranging from targeted marketing to preventative maintenance to fraud detection. The course emphasizes defining viable machine learning problems and assessing machine learning models. The class will function like a lab in which students get hands-on experience with cutting-edge analytics processes, tools, and techniques. This firsthand experience provides students with insight into how big data analytics can (and can't) be used for business processes and decision-making.
Students will leave the program familiar with machine learning’s concepts, vocabulary, and applications. Students will understand what cutting-edge machine learning can and cannot do, and what existing challenges forthcoming technologies may help solve. Through exposure to machine learning tools and techniques, students will learn what data scientists actually do.
After completing the course, students should be able to look "backward," from business objectives through a machine learning modeling process to a data model. Conceptualizing a business problem as a data structure used by ML routines is the course's most critical objective.
Armed with the knowledge of what machine learning does and how data scientists use it, students will be better able to collaborate with dedicated data scientists. Aware of machine learning’s possibilities and processes, students will be better prepared to work with and lead cross-functional, data-driven teams.
Data science, machine learning, artificial intelligence, cognitive computing, and big data are all evolving rapidly. These rapid technology changes have created a chasm between individuals with functional expertise and individuals with technological/algorithmic expertise. Through exposure to the tasks, techniques, and problems characteristic of machine learning, students will become familiar with what these cutting edge technologies can do. This familiarity will allow students to formulate reasonable data science expectations and to critically assess data science analyses.
This is an intensive, in-person, 4-day course. Before the course begins, students must read a background text to ensure a common vocabulary and familiarity with basic techniques. Most of the course will occur like a workshop, in which we collectively explore tools and reflect on their strengths, weaknesses, and potential strategic application. Because discovery/surprise are so important in this process, most slides and materials will NOT be made available in advance of class.
Before the class, the professor will post several short videos. These cover crucial statistical background knowledge, and must be watched in advance of the class.
Day 1: Defining ML and Isolating ML-suitable problems
We will spend day 1 specifying what machine learning (ML) is, what it can do, and how it interfaces with the larger organization. We will answer questions including: What is machine learning (ML)? How is ML distinct from classic scientific inquiry? From other elements of the cognitive computing universe? What are the fundamental techniques/algorithms in ML? With this definition of ML and its techniques, to what types of problems does the toolset lend itself? How does ML interface with the organization’s larger digital ecosystem?
Day 2: Data, Modeling, and Model Assessment I
This day will present the “nitty gritty” of how machine learning is done – we will explore how the algorithms and data model work. We will begin the day reviewing data storage, collection, and transformation. We will then execute some modeling exercises stepwise before a discussion about what constitutes a “good” model. This discussion will cover important technical topics such as overfit, cross-validation, and target leak. Finally, we will introduce automated ML.
Day 3: Data, Modeling, and Model Assessment II
In day 3, we will introduce more tools to conduct machine learning, which we will use to conduct several illustrative machine learning projects. Distinguishing model evaluation from model valuation, we will introduce techniques to assign value to models, and discuss that as a bridge between granular model output and higher-level strategic insight.
Day 4: Reflecting High-Level Implications of Modeling Nuances
On day 4, we will discuss sources of cultural and institutional resistance to ML adoption. We will have a final student exercise, in which students apply their learning and present strategic insights from a machine learning project.
The course is conducted as a teaching module, where students have classes all day for four consecutive days, a total of 32 hours.
Students will need a local Python installation. The instructor recommends and will support Continuum’s cross-platform Anaconda distribution, but any active, up-to-date Python environment will work.
The course is a part of a full MBA programme and examination in all courses must be passed in order to obtain a certificate.
In all BI Executive courses and programmes, there is a mutual requirement for the student and the course responsible regarding the involvement of the student's experience in the planning and implementation of courses, modules and programmes. This means that the student has the right and duty to get involved with their own knowledge and practice relevance, through the active sharing of their relevant experience and knowledge.
Granted admission to the BI-Fudan MBA programme. Please consult our student regulations.
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
Form of assessment:
Examination when next scheduled course
|3-hour, in-class exam, counts 100% of the final grade.
|Form of assessment:
|3-hour, in-class exam, counts 100% of the final grade.
|Examination when next scheduled course
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 4 ECTS credit corresponds to a workload of at least 110 hours.