Electrical Engineering Graduate Certificate at Stanford University
Electrical Engineering Graduate Certificate
Certificate degree at Stanford University
Program Details
Explore the Electrical Engineering Graduate Certificate program at Stanford University. This program is offered in USA and provides an excellent learning opportunity in Certificate studies.
The Electrical Engineering Graduate Certificate from Stanford University offers a flexible pathway for individuals seeking to enhance their knowledge and skills in electrical engineering without committing to a full Master's degree. This program allows students to select courses that align with their interests, covering fundamental concepts and advanced topics in circuits, software and hardware systems, communications, and signal processing. Students are expected to complete four graduate courses within three academic years, with at least two courses from the Electrical Engineering department. The program is designed to accommodate a time commitment of approximately 15-20 hours per week for lectures and assignments, and most students complete the certificate in 1-2 years. Upon successful completion, participants earn a Stanford Graduate Certificate, which is accredited and can be applied towards a relevant graduate degree program.
- University
- Stanford University
- University Location
- USA (Online)
- Program Duration
- 2.0 years
- Ranking
- #6
- Part-time allowed
- Yes
Required Courses
- Advanced Integrated Circuit Design (EE214B)
- Fundamentals of Analog Integrated Circuit Design (EE214A)
- Advanced VLSI Devices (EE316)
- Analog-Digital Interface Circuits (EE315)
- Advanced Integrated Circuits Technology (EE311)
- Emerging Non-Volatile Memory Devices and Circuit Design (EE309B)
- Semiconductor Memory Devices and Circuit Design (EE309A)
- Autonomous Implantable Systems (EE303)
- Digital Systems Engineering (EE273)
- Introduction to VLSI Systems (EE271)
- Principles and Models of Semiconductor Devices (EE216)
Elective Courses
- Introduction to Internet of Things (EE284A)
- Computer Systems Architecture (EE282)
- Digital Systems Engineering (EE273)
- Introduction to Cryptography (CS255)
- Mining Massive Data Sets (CS246)
- Deep Learning for Computer Vision (CS231N)
- Computer Vision: From 3D Reconstruction to Recognition (CS231A)
- Machine Learning (CS229)
- Probabilistic Graphical Models: Principles and Techniques (CS228)
- Artificial Intelligence: Principles and Techniques (CS221)
- Introduction to Digital Communication (EE279)
- Wireless Communications (EE359)
- Digital Communication (EE379)
- Information Theory (EE276)
- Biochips and Medical Imaging (EE225)
- Introduction to Micro and Nano Electromechanical Systems (ENGR240)
- Decision Making Under Uncertainty (AA228)
- The Fourier Transform and Its Applications (EE261)
- Introduction to Linear Dynamical Systems (EE263)
- Introduction to Statistical Signal Processing (EE278)
- Convex Optimization I (EE364A)
- Computational Imaging (EE367)
- Medical Imaging Systems II (EE369B)
- Introduction To Control Design Techniques (ENGR205)
- Image Systems Engineering (PSYCH221)
- Modern Applied Statistics: Learning II (STATS315B)
Online Program
This program can be completed entirely online.