Machine learning is an exciting and fast-moving field of computer science with many recent consumer applications (e.g., Microsoft Kinect, Google Translate, Iphone’s Siri, digital camera face detection, Netflix recommendations, Google news) and applications within the sciences and medicine (e.g., predicting protein-protein interactions, species modeling, detecting tumors, personalized medicine). This course introduces undergraduate computer science students to the field of machine learning. Students learn about the theoretical foundations of machine learning and how to apply machine learning to solve new problems. Assuming no prior knowledge in machine learning, the course focuses on two major paradigms in machine learning which are supervised and unsupervised learning. In supervised learning, we learn various methods for classification and regression. Dimensionality reduction and clustering are discussed in the case of unsupervised learning

Computer Science (Undergraduate)
4 credits – 13 Weeks

Sections (Fall 2024)

CSCI-UA 9473-000 (2626)
09/02/2024 – 12/05/2024 Tue
8:00 AM – 11:00 AM (Morning)
at NYU Paris (Global)
Instructed by

CSCI-UA 9473-000 (2627)
09/02/2024 – 12/05/2024 Thu
10:00 AM – 11:00 AM (Morning)
at NYU Paris (Global)
Instructed by