From healthcare, marketing, and HR to finance and manufacturing, AI is changing the way we live and work. As a consequence, the demand for expertise in AI and machine learning is growing rapidly. This course will enable students to take the first step toward building AI driven applications.
The course’s main topics are:
- What machine learning, deep learning and AI are.
- When machine learning is the right tool for AI.
- How to select the right machine learning algorithm for your AI scenario.
- How to use Python libraries to build AI applications.
- How to use Automated Machine Learning and Python to build AI applications.
- Real-world AI use cases and applications.
This course aims at teaching the most important concepts of the machine learning workflow that data scientists follow to build end-to-end data science solutions. We assume that students have basic knowledge of linear algebra and calculus.
Students will gain exposure to the theory behind classification, regression, forecasting, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own. The course includes asynchronous work, which students are expected to complete between class sessions.
Student computers can either be a PC or a Mac, but should have at least 10GB of free space.
Francesca Lazzeri, PhD is an experienced scientist and machine learning practitioner with over 12 years of both academic and industry experience. She is author of a number of publications, including technology journals, conferences, and books. She currently leads an international team of cloud AI advocates and developers at Microsoft, managing a large portfolio of customers in the academic/education sector and building intelligent automated solutions on the cloud.
Before joining Microsoft, she was a research fellow at Harvard University in the Technology and Operations Management Unit. She is also advisory board member of Global Women in Data Science (WiDS) initiative, machine learning mentor at the Massachusetts Institute of Technology and Columbia University, and active member of the AI community.
Specific course details such as topics, activities, hours, and instructors are subject to change at the discretion of the University. Not all instructors listed for a course teach all sections of that course.