The overall goal of the course is to provide a foundation and framework for understanding how to use machine learning models in data-driven decision making. The course will include some theoretical background, but will be application-focused. The course will survey key technologies and applications that are driving the ML revolution. The course will look in-depth at all three types of ML: supervised (including classification and deep learning), unsupervised (including association rule learning and dimensionality reduction), and reinforcement learning. This new course will introduce machine learning (ML) concepts, with a heavy focus on business applications.
Leading and managing cultural change (organizational and individual level change management). Defining and creating a culture of analytics. Organizing for innovation (intrapreneurship). Implementation essentials (link to project leadership course). Creating a Business Model Canvas (for an entrepreneurial venture). Creating and implementing a strategic plan (for an existing enterprise or function). How to set strategy via an agile strategic planning process. The course also touches on strategy and change as they relate to intra- and entrepreneurial endeavors. marketing, finance) through to enterprise/corporate level changes to strategy and culture. The course covers the entire spectrum of enterprise strategic and cultural transformation, including functional level changes in strategy (e.g. The course integrates 2 complementary aspects of driving organizational success through analytics – what to do (the strategy piece) and how to make it happen (the change management piece). Delivered towards the end of the program, the course provides students with the opportunity to synthesize their learnings and understand what to change and how to do it. This course covers the role strategy development and change management play in successfully capitalizing on the promise of Analytics. An appreciation for various types of analytics based new ventures and innovations. The ability to 'pitch' a business opportunity in order to gain whatever resources are necessary to execute on the opportunity presented. The ability to understand what strategies and resources are required and available to translate a viable opportunity into a real business. The ability to differentiate, using a systematic and thorough approach, between an idea and a true business opportunity, the ability to assess an analytics-based new business venture or corporate innovation. Ideation techniques such as Design Thinking. Students will finish the course with the following: The course will provide a unique opportunity for students to immerse themselves in what it means to be entrepreneurial, and in the entire new venture context. This course introduces students to entrepreneurship and innovation, designed to embed a much greater appreciation for the role of entrepreneurial thinking and know-how in the minds of all students, regardless of current or desired role in business – start-up or corporate innovation. The phenomenal power for problem analysis provided by modern spreadsheets will be exploited in the course, using EXCEL and EXCEL Add-Ins and Precision Tree). Case studies will help in developing facility with model formulation and interpretation of results, and will aid development of an intuition about effective use of modeling. We will examine models from each of these categories, chosen on the basis of degree of use in current practice. 
Decision models can be divided into two main categories: those that assume that the variables within, and outcomes from, a decision problem are known with certainty (called deterministic models), and those which introduce elements of uncertainty or risk (called stochastic models). The intention of the course is to help you become a perceptive and critical user of quantitative models in an organization. We will not focus on algorithmic details of specific model solution, but rather will use pre-tested computer routines in most cases. In this course, we shall concentrate on the processes of problem recognition, model formulation, and interpretation of the model results and implementation. Finally, the model results are applied back to the original managerial problem, or implemented.
This model will be manipulated, or "solved", to identify the decision that yields the "best" outcome. The general approach we will follow will be to construct an analytical representation of the problem, called a model. In this course, we will explore the use of a variety of analytical methods to assist in the mechanics of problem solving and case studies and illustrations to illuminate contextual issues. Good managerial decisions depend on an understanding not only of the problem structure, parameters, and constraints, but also of the organizational context in which the decision will be implemented.
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