π Course: Introduction to Electrical Engineering
π Overview
High-level summary of the course. What are the main learning objectives?
lectures
| File | Topic | Status | Created | this.file.link |
|---|---|---|---|---|
| lecture_EE1G1_lec1_math_echelon_2025-09-01 | Basis Matrix | To Do | September 01, 2025 | EE1G1 |
| lecture_EE1G1_lec3_history_2025-09-09 | history | Pending | September 09, 2025 | EE1G1 |
π§ͺ Labs
| File | Status |
|---|
π Assignments
| File | Status | Due Date |
|---|---|---|
| 2025-10-07_EE1G1_epilepsy_system | To Do | October 07, 2025 |
| 2025-09-02_EE1G1_intgrated-math | To Do | November 09, 2025 |
π― Learning Objectives
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group by function task.file.filenameWithoutExtensionπ‘ Topics & Concepts
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β Exam Readiness Checklist
| Topic / Skill | Know for Certain | I Think I Know | Need to Study |
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π Resources & Links
- Syllabus:
- Professor Contact:
- Study Group Notes:
π Study guide
Description
- Purpose: Introduces Electrical Engineering (EE) and the EE BSc program at TU Delft.
- Components: The course is structured around four main activities:
- Lectures (in-person)
- Programming sessions (online)
- Integrated math (in-person)
- Mentorship (in-person)
Learning objectives
At the end of this course, students should be able to:
- Define EE fields
- Example:
graph TD A[Electrical Engineering] --> B(Power Systems) A --> C(Electronics) A --> D(Telecommunications) A --> E(Control Systems)
- Recognize critical challenges in the field
- Example: π‘ Challenges: Energy Efficiency, Cybersecurity, Sustainable Technologies, AI Integration.
- Identify key contributions of EE to society
- Example: π Contributions: Internet, Renewable Energy, Medical Imaging, Smart Grids.
- Classify programming languages
- Example:
graph TD A[Programming Languages] --> B(High-Level) A --> C(Low-Level) B --> D(Python) B --> E(Java) C --> F(Assembly) C --> G(Machine Code)
- Constructing python codes
- Example:
# Simple Python Code
def greet(name):
return f"Hello, {name}!"
print(greet("World"))- Discuss and reflect on teamwork, ethics, academic integrity, study skills (math, time management)
- Example: Key Soft Skills:
- π€ Teamwork: Collaborative problem-solving.
- βοΈ Ethics & Integrity: Responsible conduct in engineering.
- β° Study Skills: Effective time management for complex topics.
- Example: Key Soft Skills:
And gain the following mathematical competences / skills:
- Solve systems of linear equations using row reduction;
- Example:
- Evaluate a one- or two-sided limit of a function at a point or at infinity;
- Example:
- Interpret the continuity of a function;
- Example: A function is continuous if you can draw its graph without lifting your pen.
- Evaluate derivatives of elementary functions and sums, products, quotients and compositions of these functions;
- Example: If , then
- Use the derivative to describe the qualitative behaviour of a function;
- Example: If , the function is increasing.
- Find the linearization of a function;
- Example: Linearization of at :
- Evaluate definite and indefinite integrals of elementary functions and of (piecewise) continuous functions;
- Example: and
- Evaluate definite and indefinite integrals using integration by parts;
- Example:
- Perform arithmetics involving complex numbers;
- Example:
- Determine the polar and exponential form of a complex number;
- Example:
- Find complex solutions of polynomial expressions;
- Example: Solutions to are and .
- Interpret a direction field of a first order differential equation;
- Example: A direction field (or slope field) shows small line segments at various points, indicating the slope of the solution curve passing through that point for a given differential equation.
- Solve a first or second order, linear, homogeneous or nonhomogeneous differential equation or initial-value problem with constant coefficients;
- Example: (First-order linear homogeneous DE)
- Describe the movement of a mass-spring system using a second order, linear differential equation infer correspondences with circuit theory.
- Example: Mass-spring system:
Teaching method
This course has blended design and is composed of in person lectures, mentorship hours, online sessions. Students are also required to complete take-home assignments.
Contact hours per week
8/0/0/0
Assessment
This course is a Pass/Fail course. In order to pass the course, students are required to participate in all four components of the course (integrated math, programming, lectures, mentorship) and
- Complete all integrated math assignments
- Complete all python assignments
- Complete all Inspirational Lecture assignments
- Attend 75% Mentorship activities and complete 75% of Mentorship assignments
Students are expected to work individually on all assignments. Each student is responsible (being the sole author) for the content of their own deliverables. The documentation and code handed in must be self-written.
- If applicable, the deliverable should indicate whether and which AI tools were used, and how they were used. AI tools are not allowed to generate code, interpret results, or produce text for the assignment.
- More course-specific information and AI usage guidelines will be provided on the course Brightspace page.
In case of insufficient results for the assignments, a repair option may exist in accordance in with Article 2, Examination requirements, Clause 4, of the Implementation Regulations 2024-2025.
Disclaimer: information may change depending on unforeseen circumstances or measures (see: TER Art 29, sub 4).