Artificial Intelligence (AI) Principles and Practices BI101 Why Attend Organizations are creating an avalanche of data, and with Artificial Intelligence (AI) technology we can put that data to work in order to increase benefits and reduce costs. With modern technology we can use structured and unstructured data and apply Artificial Intelligence to bring new possibilities to improve decision making, improve company performance and augment human capabilities. However, this new field of science comes with new terminologies, technologies, jobs and organizational processes. This course provides participants with the AI literacy to be the AI leader in their organizations, to understand AI concepts, to converse on a qualified level with the data specialists, to create an AI strategy, to know how to set up and run an AI project and to assess the make or buy decision of tooling. Course Methodology This courses applies a variety of interactions, ranging from team-work on case studies, to individual work on applying templates to their own experience, to group discussions about joint challenges. Course Objectives By the end of the course, participants will be able to: Explain AI as a concept and all its manifestations Apply the different AI appearances in the business value chain Demonstrate the technologies and algorithms behind AI Apply best practices in an AI project with its activities Assess the available and necessary skills and competencies Discuss on a qualified level with business and data specialists on relevant topics Target Audience This course is designed for senior and middle management who recognize that digital transformation is unavoidable; and for those who understand that continuous improvement, innovation and disruption is part of doing business and want to be prepared and reap the benefits of Artificial Intelligence. Understanding of basic technology concepts such as data and cloud is recommended but not required. In short, this course is for managers wanting to identify what AI can do for them and to drive Digital Transformation, rather than understand the technical methodologies of what happens underneath its hood. Course Outline Introduction to Artificial Intelligence (AI), Machine Learning (ML) and data science AI as a concept and appearances AI as a combination of technologies AI in historical perspective AI: sense, reason, act The thinking in AI: Machine learning 9 building blocks Algorithms and Engines Supervised learning and applications Classification: Algoritms like Naïve bayes Regression: Algorithms like Linear regression and decision trees Semi supervised learning and applications Algorithms like Q-Learning, SARSA Unsupervised learning and applications Clustering: Algorithms like kMeans and hierarchical Defining an AI approach: teamwork Practice with building blocks and use cases Reflection and application to own organization Creative garage approach to ideate and define an AI Project AI opportunity matrix Successful use cases by Porter’s value chain Primary activities: Inbound operations and outbound marketing and sales and service Supporting activities: Admin and finance, HR, research and development, procurement Successful use cases by technology NLP Image recognition Machine learning Running successful AI projects Project process Ideation & problem definition Exploratory data analysis Model development Implementation Skills and capabilities Organizational changes 10 pitfalls AI tooling and roadmap Technologies: R, Python, Spotfire, Hadoop etc Platforms: Ms Azure, IBM Watson, Google Tensorflow Roadmap development Prepare your first roadmap Develop your strategy and tactics to realize an AI project funnel
