Learn · MIT OpenCourseWare

Three images, one of the completed robot with light-tracking head, and close-ups of the breadboard and Lego head.

Course6.01SC54 lectures34 h 58 min

Introduction to Electrical Engineering and Computer Science I

Prof. Leslie Kaelbling and others · Massachusetts Institute of Technology

Start with lecture 1 At MIT OpenCourseWare

About

This course provides an integrated introduction to electrical engineering and computer science, taught using substantial laboratory experiments with mobile robots. Our primary goal is for you to learn to appreciate and use the fundamental design principles of modularity and abstraction in a variety of contexts from electrical engineering and computer science.

Our second goal is to show you that making mathematical models of real systems can help in the design and analysis of those systems. Finally, we have the more typical goals of teaching exciting and important basic material from electrical engineering and computer science, including modern software engineering, linear systems analysis, electronic circuits, and decision-making.

Course Format

This course has been designed for independent study. It includes all of the materials you will need to understand the concepts covered in this subject. The materials in this course include:

Lecture videos from Spring 2011, taught by Prof. Dennis Freeman

Recitation videos, developed for OCW Scholar by teaching assistant Kendra Pugh

Course notes

Software and design labs

Homework assignments and additional exercises

Nano-quizzes and exams with solutions

Content Development

Leslie Kaelbling

Lectures

Object-Oriented Programming

  1. Lecture 1: Object-Oriented Programming 1 h 18 min · Video
  2. Recitation 1: Object-Oriented Programming 12 min · Video
  3. Recitation 2: Inheritance 14 min · Video

State Machines

  1. Lecture 2: Primitives, Combination, Abstraction, and Patterns 1 h 21 min · Video
  2. Recitation 3: Python Notables 17 min · Video
  3. Recitation 4: State Machines 14 min · Video

Signals and Systems

  1. Lecture 3: Signals and Systems 1 h 20 min · Video

Linear Time-Invariant Signals and Systems

  1. Recitation 5: LTI Motivations and Representations 13 min · Video
  2. Recitation 6: System Equivalences 16 min · Video

Characterizing System Performance

  1. Lecture 5: Characterizing System Performance 1 h 19 min · Video
  2. Recitation 7: Poles, Part I 10 min · Video
  3. Recitation 8: Poles, Part II 16 min · Video

Designing Control Systems

  1. Lecture 6: Designing Control Systems 1 h 15 min · Video

Circuits

  1. Lecture 7: Circuits 1 h 19 min · Video
  2. Recitation 9: Circuits: Representation, KVL, KCL 18 min · Video
  3. Recitation 10: Circuits: NVCC, Sample Problem 14 min · Video

Op-Amps

  1. Lecture 8: Op-Amps 1 h 20 min · Video
  2. Recitation 11: Op-Amps 11 min · Video

Circuit Abstractions

  1. Lecture 9: Circuit Abstractions 1 h 12 min · Video
  2. Recitation 12: Thevenin/Norton Equivalence and Superposition 13 min · Video

Discrete Probability

  1. Lecture 10: Discrete Probability and State Estimation 1 h 18 min · Video
  2. Recitation 13: Probability: Basics 16 min · Video

State Estimation

  1. Recitation 14: Probability: State Estimation 13 min · Video

Search Algorithms

  1. Lecture 12: Search Algorithms 1 h 9 min · Video
  2. Recitation 15: Search: Basics 16 min · Video

Optimizing a Search

  1. Lecture 13: Optimizing a Search 1 h 4 min · Video
  2. Recitation 16: Search: Dynamic Programming, Costs and Heuristics 15 min · Video

Resource Index

  1. Lecture 1: Object-Oriented Programming 1 h 18 min · Video
  2. Recitation 1: Object-Oriented Programming 12 min · Video
  3. Recitation 2: Inheritance 14 min · Video
  4. Lecture 2: Primitives, Combination, Abstraction, and Patterns 1 h 21 min · Video
  5. Recitation 3: Python Notables 17 min · Video
  6. Recitation 4: State Machines 14 min · Video
  7. Lecture 3: Signals and Systems 1 h 20 min · Video
  8. Recitation 5: LTI Motivations and Representations 13 min · Video
  9. Recitation 6: System Equivalences 16 min · Video
  10. Lecture 5: Characterizing System Performance 1 h 19 min · Video
  11. Recitation 7: Poles, Part I 10 min · Video
  12. Recitation 8: Poles, Part II 16 min · Video
  13. Lecture 6: Designing Control Systems 1 h 15 min · Video
  14. Lecture 7: Circuits 1 h 19 min · Video
  15. Recitation 9: Circuits: Representation, KVL, KCL 18 min · Video
  16. Recitation 10: Circuits: NVCC, Sample Problem 14 min · Video
  17. Lecture 8: Op-Amps 1 h 20 min · Video
  18. Recitation 11: Op-Amps 11 min · Video
  19. Lecture 9: Circuit Abstractions 1 h 12 min · Video
  20. Recitation 12: Thevenin/Norton Equivalence and Superposition 13 min · Video
  21. Lecture 10: Discrete Probability and State Estimation 1 h 18 min · Video
  22. Recitation 13: Probability: Basics 16 min · Video
  23. Recitation 14: Probability: State Estimation 13 min · Video
  24. Lecture 12: Search Algorithms 1 h 9 min · Video
  25. Recitation 15: Search: Basics 16 min · Video
  26. Lecture 13: Optimizing a Search 1 h 4 min · Video
  27. Recitation 16: Search: Dynamic Programming, Costs and Heuristics 15 min · Video

Syllabus and materials

At MIT OpenCourseWare, as the course publishes them.

Credits and licence

Institution
Massachusetts Institute of Technology
Department
Electrical Engineering and Computer Science
Taught by
Prof. Leslie Kaelbling, Prof. Jacob White, Prof. Harold Abelson, Prof. Dennis Freeman, Prof. Tomás Lozano-Pérez, Prof. Isaac Chuang
Term
Spring 2011
Level
Undergraduate
Language
English
Subjects
Computer science, Engineering, Art, architecture and design
Source
MIT OpenCourseWare
Licence
CC BY-NC-SA 4.0, as MIT OpenCourseWare states it. Non-commercial use only. MIT OpenCourseWare's terms
Attribution
Prof. Leslie Kaelbling, Prof. Jacob White, Prof. Harold Abelson, Prof. Dennis Freeman, Prof. Tomás Lozano-Pérez, Prof. Isaac Chuang. Introduction to Electrical Engineering and Computer Science I. Spring 2011. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu/. License: Creative Commons BY-NC-SA.
Who can watch
MIT OpenCourseWare gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed