Emily Mower ProvostAssociate Professor, Electrical Engineering and Computer Science
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Emily Mower Provost
Emily Mower ProvostAssociate Professor, Electrical Engineering and Computer ScienceElectrical Engineering and Computer Science
(734) 647-1802 3629 Beyster Bldg.2260 Hayward St.Ann Arbor, MI 48109-2122

Teaching

EECS 498

Intelligent Interactive Systems (IIS): Ubiquitous Computing Using Sensor-Based Environments
Major Design Experience
Winter, 2015

This course covers the concepts and techniques that underlie successful interactive user environments including facial expressions, body gestures, phone-based sensing, environmental sensing, and speech. Topics include: speech modeling, recognition, and interactive computing. Fluency in a standard object-oriented programming language is assumed. Prior experience with speech or other data modeling is neither required nor assumed.

Please visit the course page.

EECS 498

Intelligent Interactive Systems
Winter, 2014

This course covers the concepts and techniques that underlie successful interactive user environments with a focus on speech-based systems. Topics include: speech modeling and recognition, environmental sensing and modeling, and interactive computing. Fluency in a standard object-oriented programming language is assumed. Prior experience with speech modeling is not required.

EECS 492

Introduction to Artificial Intelligence
Fall, 2012

This course is an introductory course to artificial intelligence. The purpose of this course is to provide an overview of this field. We will cover topics including: problem solving, knowledge, reasoning, and planning, uncertainty, learning, and application. The goals of this course are to provide a fundamental knowledge of the field and practical implementation experience.

EECS 598

Applications of Machine Learning in Human-Centered Computing
Winter, 2012

Human-centered computing (HCC) is the science of decoding human behavior. HCC seeks to provide a computational account of aspects of human behavior ranging from interaction patterns to individual emotion expression using techniques drawn from both signal processing and machine learning. However, the complexity of this new domain necessitates alterations to the techniques common within the machine learning field and a fundamental understanding of the domains under analysis.