Machine learning and sensors are at the core of most modern computing devices and technology. From Amazon Echo to Apple Watch to Google Photos to self-driving cars, making sense of the data coming from powerful but noisy sensors is a critical challenge. The course will aim to explore this intersection of sensors and machine learning, understand the inner workings of modern computing technologies, and design the future ones. We will cover data collection, signal processing, data processing, data visualization, feature engineering, machine learning tools, and some prototyping technologies. The course will focus on class discussions, hands-on demonstrations, and tutorials. We will evaluate students on their class participation, multiple mini-projects, and a final team project.
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The aim of this class is to introduce the students to sensing and inference. We focus on how traditional topics of computing and electrical engineering have evolved to support the vision of connected, portable, and a user-focussed computing environment. The course includes discussion into contribution of various fields, including human-computer interaction, embedded computing, computer vision, distributed systems, machine learning, signal processing, security, and privacy. We will discuss how these various disciplines are coming together to form an end-to-end system that generates useful and user-actionable data. We will take a hands-on approach towards building and evaluating these systems. The students will gain practical experience in developing sensing systems in different application domains, such as activity recognition, health sensing, gestural interaction, etc. You will learn about embedded systems and understand the advantages and limitations of different platforms.

Course Website

Machine learning and sensors are at the core of most modern computing devices and technology. From Amazon Echo to Apple Watch to Google Photos to self-driving cars, making sense of the data coming from powerful but noisy sensors is a critical challenge. The course will aim to explore this intersection of sensors and machine learning, understand the inner workings of modern computing technologies, and design the future ones. We will cover data collection, signal processing, data processing, data visualization, feature engineering, machine learning tools, and some prototyping technologies. The course will focus on class discussions, hands-on demonstrations, and tutorials. We will evaluate students on their class participation, multiple mini-projects, and a final team project.
Course Website

The aim of this class is to introduce the students to sensing and inference. We focus on how traditional topics of computing and electrical engineering have evolved to support the vision of connected, portable, and a user-focussed computing environment. The course includes discussion into contribution of various fields, including human-computer interaction, embedded computing, computer vision, distributed systems, machine learning, signal processing, security, and privacy. We will discuss how these various disciplines are coming together to form an end-to-end system that generates useful and user-actionable data. We will take a hands-on approach towards building and evaluating these systems. The students will gain practical experience in developing sensing systems in different application domains, such as activity recognition, health sensing, gestural interaction, etc. You will learn about embedded systems and understand the advantages and limitations of different platforms.

Course Website

The aim of this class is to introduce the students to sensing and inference. We focus on how traditional topics of computing and electrical engineering have evolved to support the vision of connected, portable, and a user-focussed computing environment. The course includes discussion into contribution of various fields, including human-computer interaction, embedded computing, computer vision, distributed systems, machine learning, signal processing, security, and privacy. We will discuss how these various disciplines are coming together to form an end-to-end system that generates useful and user-actionable data. We will take a hands-on approach towards building and evaluating these systems. The students will gain practical experience in developing sensing systems in different application domains, such as activity recognition, health sensing, gestural interaction, etc. You will learn about embedded systems and understand the advantages and limitations of different platforms.

Course Website

The aim of this class is to introduce the students to ubiquitous computing. We will focus on how traditional topics of computing have evolved to support the vision of a connected, portable, and a human-centric computing environment. The course will include discussion into contribution of various fields, including human-computer interaction, embedded computing, computer vision, distributed systems, machine learning, and electrical engineering. The students will gain practical experience in developing sensing systems in different application domains, such as activity recognition, health sensing, gestural interaction, etc.

Course Website

The aim of this class is to introduce the students to sensing and inference. We focus on how traditional topics of computing and electrical engineering have evolved to support the vision of connected, portable, and a user-focussed computing environment. The course includes discussion into contribution of various fields, including human-computer interaction, embedded computing, computer vision, distributed systems, machine learning, signal processing, security, and privacy. We will discuss how these various disciplines are coming together to form an end-to-end system that generates useful and user-actionable data. We will take a hands-on approach towards building and evaluating these systems. The students will gain practical experience in developing sensing systems in different application domains, such as activity recognition, health sensing, gestural interaction, etc. You will learn about embedded systems and understand the advantages and limitations of different platforms.

Course Website

Courses at University of Washington