Basics of Artificial Intelligence

Course Description

This course is intended for SEAS Engineering graduate students who are interested in understanding the fundamental issues, challenges and techniques that are associated with recent advances in Artificial Intelligence (AI). The course will discuss the history and properties of basic AI systems including neural network, machine learning, and data science, how to build a basic machine learning and AI project including data scrapping, data processing etc. We will discuss the challenges of bias, security, privacy, explainability, ethical issues, and the use of context. We will learn about AI’s use in applications such as image processing and computer vision, natural language processing, recommendation systems, and gaming. The course is supported by a primer on the use of Python, to support home works and projects related to machine learning. The course will be a combination of lectures, discussions, activities and projects that will prepare students without a computer science background to study and apply artificial intelligence tools and applications in a variety of different domains. The course is NOT intended for students who have reasonable machine learning, computer science or Python programming backgrounds. Undergraduates who wish to take this course and petition for credit, need to inquire with the SEAS graduate office.

Code

EAS 510

Credits

3

Subject

Engineering & Applied Sciences

Programs That Include This Course

• Master's Master of Science in Engineering Science: AI and Data Innovation
• Master's Master of Science in Engineering Science: Data Science