Machine learning is a key component of big data analytics, business intelligence, and fintech. This course introduces machine learning techniques and focuses on insights about structuring business problems and implementing machine learning-based solutions. This course will introduce state-of-the-art machine learning techniques, including clustering, classification, ensemble methods, and neural networks, with a special focus on their applications. Students will learn in-depth knowledge of machine learning and apply the acquired skills to deliver effective solutions. This course will elaborate the hands-on use cases of machine learning solutions for customer retention, product recommendation, risk analysis, demand prediction, etc. in businesses. Python and the state-of-the-art machine learning packages will be used as the primary tools. The issues in responsible machine learning will also be discussed.

CILO-1: Describe the main concepts in various supervised and unsupervised learning techniques. CILO-2: Identify the appropriate methods and approaches to address a given business problem. CILO-3: Develop machine learning models and deliver business solutions. CILO-4: Evaluate and diagnose machine learning models and interpret the analytical results. CILO-5: Appraise whether a machine learning project follows legal, ethical, and professional codes.