Description
Course Goals:
• To make mathematics fun.
• To flex your brain muscles.
• To remind you of things you may have forgotten, but you will need for the rest of the PMP program.
• To point you toward where you can learn more.
Course Overview:
This course is intended as a foundational course for electrical engineers, and it will cover material related to sets, functions and relations. We will also talk about calculus, sequences and convergence. We will then refresh your memory about linear algebra, differential equations and Fourier transforms. We will talk about graphs and networks. Finally, we will cover an introduction to probability and machine learning.
Lectures will be organized as a combination of theory, programming and simulation tasks, and in-class activities, focusing on real-life examples. Our programming language of choice will be Python.
• To make mathematics fun.
• To flex your brain muscles.
• To remind you of things you may have forgotten, but you will need for the rest of the PMP program.
• To point you toward where you can learn more.
Course Overview:
This course is intended as a foundational course for electrical engineers, and it will cover material related to sets, functions and relations. We will also talk about calculus, sequences and convergence. We will then refresh your memory about linear algebra, differential equations and Fourier transforms. We will talk about graphs and networks. Finally, we will cover an introduction to probability and machine learning.
Lectures will be organized as a combination of theory, programming and simulation tasks, and in-class activities, focusing on real-life examples. Our programming language of choice will be Python.
General Information
EE P 502: Analytical Methods for Electrical Engineers
Lecture time: Wednesdays from 6:00-9:00pm
• In person, in EEB 045 classroom
• Remotely via UW Zoom
Instructor: Tamara Bonaci (tbonaci@)
Office hours: Fridays from 2:30-3:30pm via Zoom, or by appointment
Teaching Assistants:
• Trung Le (tle45@)
o Office hours: TBD
• Aditya Karan (aditya04@)
o Office hours: TBD
• In person, in EEB 045 classroom
• Remotely via UW Zoom
Instructor: Tamara Bonaci (tbonaci@)
Office hours: Fridays from 2:30-3:30pm via Zoom, or by appointment
Teaching Assistants:
• Trung Le (tle45@)
o Office hours: TBD
• Aditya Karan (aditya04@)
o Office hours: TBD
Course Schedule/Topical Outline:
Week 1: Course overview. Introduction to Python for analysis.
Week 2: Sets, functions, relations and cardinality.
Week 3: Calculus, sequences, and convergence.
Week 4: Introduction to linear algebra.
Week 5: Linear algebra II. Introduction to differentiation.
Week 6: Differentiation and differential equations.
Week 7: Introduction to Fourier transforms and analysis.
Week 8: Introduction to graphs and networks.
Week 9: No lecture – Thanksgiving.
Week 10: Introduction to probability.
Week 11: Introduction to machine learning. Course review.
Finals week: Final projects due.
Week 2: Sets, functions, relations and cardinality.
Week 3: Calculus, sequences, and convergence.
Week 4: Introduction to linear algebra.
Week 5: Linear algebra II. Introduction to differentiation.
Week 6: Differentiation and differential equations.
Week 7: Introduction to Fourier transforms and analysis.
Week 8: Introduction to graphs and networks.
Week 9: No lecture – Thanksgiving.
Week 10: Introduction to probability.
Week 11: Introduction to machine learning. Course review.
Finals week: Final projects due.
Grading:
Your grade in this course will be based on homework assignments, quizzes and final project. The expected grade breakdown is:
• Homework assignments: 65%
• Final project: 25%
• Quizzes: 10%
• Homework assignments: 65%
• Final project: 25%
• Quizzes: 10%
| Name | Office Hours | |
|---|---|---|
Aditya Karan | When? Where? | |
Tamara Bonaci | When? Where? | |
Trung Le | When? Where? |