Wearable Devices and Computer Vision

Harvard Summer School

DGMD S-14

Section 1

CRN 34484

View Course Details
This course explores the field of wearable devices and computer vision, and exposes students to hands-on practical exercises based on real-life situations and industry problems. Wearable technologies is currently a $50 billion industry, with estimated annual growth of 10% year over year. It is experiencing explosive growth with exciting applications in many fields, from medicine to sports to fitness to entertainment, empowering people to interact, communicate, and experience the environment around them in new, innovative ways. Some prominent examples are smart watches, medical trackers, and augmented reality (AR) and virtual reality (VR) headsets. Underlying these advances in wearable devices is computer vision, which is an exciting field of artificial intelligence (AI) and machine learning enabling computers to derive information from images, videos, and other inputs. In this course we explore advances in wearable devices and utilize computer vision to tackle emerging problems (for example, assistive devices, educational applications, and health monitoring). Students learn about sensors, signal processing, data analytics, AI, machine learning, computational optical analysis, simultaneous localization and mapping, lighting and material estimation, and robust algorithms for modeling.

Instructor Info

Nabib Ahmed, AM

Artificial Intelligence Researcher, Meta


Meeting Info

TTh 6:30pm - 9:30pm (6/24 - 8/9)

Participation Option: Online Asynchronous or Online Synchronous

In online asynchronous courses, you are not required to attend class at a particular time. Instead you can complete the course work on your own schedule each week.

Deadlines

Last day to register: June 20, 2024

Prerequisites

CSCI S-7, CSCI S-50, or equivalent. Experience with programming, technical and code documentation, and data (any programming language will do; some examples are Python, R, Java, or C/C++). Familiarity with algebra and geometry. No background needed in machine learning, computer vision, or wearable devices.

Notes

This course meets via web conference. Students may attend at the scheduled meeting time or watch recorded sessions asynchronously. The recorded sessions are typically available within a few hours of the end of class and no later than the following business day. Open to admitted Secondary School Program students by petition.

Syllabus

All Sections of this Course

CRN Section # Participation Option(s) Instructor Section Status Meets Term Dates
16693 1 Online Asynchronous, Online Synchronous Nabib Ahmed Open T 5:30pm - 7:30pm
Sep 3 to Dec 21
34484 1 Online Asynchronous, Online Synchronous Nabib Ahmed Field not found in response. TTh 6:30pm - 9:30pm
Jun 24 to Aug 9