Can computers learn? And if so, how can a computer learn? In this course, you will be introduced to machine learning, a technique which applies algorithms that enable systems to “learn by example”. Google search utilizes it to complete the search criterion as you are typing, and it enables Netflix to make good movie recommendations. Machine learning is prevalent in many fields: autonomous driving, detecting credit card fraud and cyber attacks, and organizing/searching through your every growing set of photos on your phone. Recent advances in generative AI (chatGPT by https://openai.com) have had tremendous impacts on soceity.

Using machine learning foregoes writing very complex functional programs and instead utilizes one of a set of core learning algorithms. For example, imagine writing a traditional program to decode a zip code from a picture of an envelope. Machine learning utilizes examples of hand written zip codes (digits) and “learns” from these examples to recognize/decode zip codes from millions of pieces of mail each day. This course focuses on a balance of theoretical and practical knowledge. Popular methods such as decision trees, baysian learning, and neural networks will be covered. Small projects will allow students to apply these techniques and showcase their results in a quantitative manner. Data science, which includes the field of machine learning, is the fastest growing subfield in computer science (with the highest predicted job growth over the next 10 years), so, I hope you will join me for this exciting course.