Introduction to Machine Learning and Algorithmic Bias

О курсе

This course explores the intersection of artificial intelligence (AI), machine learning (ML), and responsible business practice in our increasingly AI-driven economy. Participants establish foundational understanding of AI and ML concepts, their real-world applications, and factors driving their widespread adoption across industries. The course presents the machine learning process—from data collection and preparation through model development and evaluation—providing practical insights into how data transforms into actionable business insights. Significant attention is dedicated to algorithmic bias, a critical challenge that can undermine system effectiveness and create unintended disparities in AI applications. Through examination of real-world cases across sectors such as recruitment, healthcare, and financial services, participants learn to identify different types of bias—historical bias, representation bias, and measurement bias—and understand their business implications. The course concludes with practical strategies for bias detection and mitigation, along with governance frameworks for AI deployment. Participants gain the knowledge needed to build AI systems that work effectively for diverse populations while delivering reliable business value, preparing future leaders to harness AI's transformative potential while managing its risks and ensuring broad accessibility. This course is best suited for individuals seeking to advance their careers through skill-building, industry application, and network expansion. Whether aiming for a promotion, transitioning to a new career, or growing one’s professional skills, learners will gain valuable insights into how they can contribute to their organizations and articulate those ideas with peers, recruiters, and other stakeholders.

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Venkat Kuppuswamy

Associate Professor of Entrepreneurship and Innovation