Recently Introduced Classes and Topics in Electrical & Computer Engineering
14:332:436:06 & 16:332:579:06 Biomedical Technologies: Design and Development
Taught by Professor Umer Hassan
14:332:493:05 & 16:332:579:04 Quantum Computing Algorithms
Taught by Professor Emina Soljanin
14:332:493:06 & 16:332:579:07 Machine Learning for IoT
Machine learning for Engineers covers various topics in machine learning, with an emphasis on fundamental statistical technique and programming. While the course will motivate the covered material through the use of various engineering applications, being an engineering student (or a particular major within engineering) is not a pre-requisite for enrollment. However, enrolled students must have taken undergraduate courses in probability theory and linear algebra. The course will also require extensive programming for reinforcement of concepts introduced in the course. In keeping with the industry standards, all programming will need to be done in notebooks [e.g., Jupyter (http://jupyter.org/) and Google Colab (https://colab.research.google.com/)] using either Julia, Python, or R (individual students will get to pick any one of these languages in most assignments). In many instances, students will be forbidden from using popular machine learning packages such as scikit-learn for assignments.
14:332:436:02 (16:332:519:02) Personalized Biosensors for Global Health
Taught by Professor Umer Hassan
This course provides a detailed background on the engineering principles used for biosensing applications in disease diagnostics, and therapeutics for global health. Fabrication and characterization of the point-of-care biosensors will be taught. The course will also introduce students to the microfluidics principles, on-chip sample processing, surface functionalization techniques and label-free detection of biomolecules. Course will highlight the development of personalized predictive systems for global health care using machine learning techniques. Course will also include case studies of POC sensors for global health-care. Finally, students will work in groups of 2-3 and will do a project on a personalized biosensor design for a specific global health application.
14:332:446:04 & 16:332:579:04 Hardware and System Security
Taught by Professor Sheng Wei
This course focuses on introductions and research discussions on hardware and system security. We will review and discuss the state-of-the-art practices and research efforts on hardware and system attacks and the effective countermeasures, in order to motivate research interests and new insights in building secure and trustworthy hardware systems. In addition, we will discuss how the advances in hardware security technologies can provide fundamental support and enhancement for software and system security. In particular, we will explore the interesting connections between hardware security and other system and application domains, such as multimedia systems, mobile computing, cloud computing, big data analytics/visualization, and Internet of things. Finally, we will conclude the course by looking into the research topics related to end user experiences while interacting with the secure hardware systems, such as fraud/spam detection and usable security.
14:332:435:04 & 16:332:579:06 Energy Efficient Machine Learning Systems
Taught by Professor Bo Yuan
Machine learning has emerged as the critical technique in a massive amount of artificial intelligence-demanded scenarios. From the view of practical deployment, design energy-efficient machine learning systems, especially the state-of-the-art deep learning system, is particularly important due to the high computation and storage requirement. This course will introduce and discuss various types of approaches, ranging from high-level algorithm to hardware architecture to underlying circuits, to address the emerging energy challenge for machine learning system design.
New classes introduced in prior semesters