Through a combination of lectures and case analyses, students will learn how to think more theoretically about machine learning. Machine learning centers on the development and application of algorithms that give computer systems the ability to automatically learn and improve from experience without being explicitly programmed. Key tasks include data preparation, algorithm selection, training, and evaluation of the algorithm’s predictions when applied to new data.
Rather than focus on approaches that prioritize key business outcome, this class is designed to target theoretical understanding. This is intended as a bridge between the educational experience students have had during their master’s program and the continued work that is required after they exit the program. Examples of applied practice will motivate the conversation and reinforce the necessity of abstract thinking in the real world.