If this MIT EECS curriculim is missing  classes and subjects like Circuits and Electronics,  hardware and software,nanoeletronics,devices circuits systems,elements of software construction,communication networks,sensing,databases,microcomputers,signal processing,computer architecture,system design,computer systems or theory,computation and cognition,Signals and Systems,Computation Structures (Digital Design),and Control Systems then review and update it and see if some of the classes/subjects I mentioned are either already in the list but stil need minor or major updates then update it , here is the list by the way it's from the same AI as you plus perplexity"Thank you for sharing the response from Perplexity! I’ve carefully reviewed and compared their list of classes, subjects, and resources with my original recommendations. Below, I will:

Compare and combine the two lists to ensure nothing is missed.
Add further recommendations (books, courses, and resources) where applicable.
Highlight the differences between the two lists and justify any changes or additions for a more well-rounded curriculum.
Comparative Review and Combined Curriculum
Core Classes
1. Introduction to Computer Science and Programming in Python
Course: 6.0001 (Both Perplexity and my list agree)
Book: Introduction to Computation and Programming Using Python by John V. Guttag (Both lists agree on this book; it's excellent for beginners).
Resource: MIT OpenCourseWare (Both lists agree).
Verdict: No changes needed. Both lists are aligned here.

2. Mathematics for Computer Science
Course: 6.042J
Book (Perplexity): Discrete Mathematics and Its Applications by Kenneth H. Rosen.
Book (My Recommendation): I didn’t explicitly recommend this course in my list but agree that Kenneth H. Rosen’s book is the gold standard.
Resource: MIT OpenCourseWare - Mathematics for Computer Science.
Improvement:
I would add "Mathematics for Computer Science" by Eric Lehman et al. (available for free here). This is the official MIT textbook for 6.042J and is a great free alternative to Rosen’s book.

3. Introduction to Algorithms
Course: 6.006
Book: Introduction to Algorithms by Thomas H. Cormen et al. (Both lists agree).
Resource: MIT OpenCourseWare - Introduction to Algorithms (Both lists agree).
Verdict: No changes needed. Both lists are aligned.

4. Computer Systems Engineering
Course: 6.033
Book (Perplexity): Computer Systems: A Programmer’s Perspective by Randal E. Bryant and David R. O'Hallaron.
Book (My Recommendation): I didn’t include 6.033 specifically in my list, but I agree that Computer Systems: A Programmer’s Perspective is the best choice for this subject.
Resource: MIT OpenCourseWare - Computer Systems Engineering.
Improvement: Add this course to my list, as it covers crucial EECS topics like operating systems, networks, and distributed systems.

5. Linear Algebra
Course: 18.06
Book (Perplexity): Linear Algebra Done Right by Sheldon Axler.
Book (My Recommendation): I didn’t include Linear Algebra in my original list, but Axler’s approach is excellent for theory-focused learners. However, for engineers, I recommend:
Introduction to Linear Algebra by Gilbert Strang (more practical and application-focused).
Resource: MIT OpenCourseWare - Linear Algebra.
Improvement: Add Linear Algebra explicitly to my list. Strang’s book is better suited for EECS students focused on applications in machine learning, signals, and systems.

6. Probability and Statistics in Engineering
Course: 6.041A
Book (Perplexity): Probability and Statistics by Morris H. DeGroot and Mark J. Schervish.
Book (My Recommendation): I didn’t include this course, but DeGroot’s book is excellent. Another great option is:
Probability and Random Processes for Electrical and Computer Engineers by Alberto Leon-Garcia.
Resource: MIT OpenCourseWare - Introduction to Probability.
Improvement: Add this course to my list. It’s essential for understanding randomness in signals, machine learning, and EE systems.

Specialized Classes
1. Machine Learning
Course: 6.036
Book (Perplexity): Pattern Recognition and Machine Learning by Christopher M. Bishop.
Book (My Recommendation): I recommended Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig, which covers broader AI topics. For pure ML, Bishop’s book is better.
Resource: MIT OpenCourseWare - Machine Learning.
Verdict: Bishop’s book is a better fit for machine learning. I accept Perplexity's recommendation here.

2. Digital Signal Processing
Course: 6.341A
Book (Perplexity): Digital Signal Processing: A Practical Approach by Emmanuel C. Ifeachor and Barrie Wills.
Book (My Recommendation): I recommended Signals and Systems by Oppenheim, which is better for foundational signals knowledge. For DSP specifically, Ifeachor’s book is excellent.
Resource: MIT OpenCourseWare - Digital Signal Processing.
Improvement: Keep both books. Oppenheim for fundamentals, and Ifeachor for DSP applications.

3. Cybersecurity
Course: 6.857
Book (Perplexity): Cryptography and Network Security: Principles and Practice by William Stallings.
Book (My Recommendation): I didn’t include cybersecurity, but Stallings’ book is an excellent choice.
Resource: MIT OpenCourseWare - Computer System Security.
Improvement: Add cybersecurity to my list. It’s a critical area of EECS today.

4. Quantum Computing
Course: 6.845
Book (Perplexity): Quantum Computation and Quantum Information by Michael A. Nielsen and Isaac L. Chuang.
Resource: edX Course on Quantum Computing.
Improvement: Add this course to my list for cutting-edge EECS topics.

Additional Areas
Hardware Engineering
Book (Perplexity): Digital Design by M. Morris Mano.
Book (My Recommendation): I suggested Digital Design and Computer Architecture by David Money Harris and Sarah L. Harris.
Verdict: Mano’s book is great for beginners, but Harris’ book provides a more modern approach to combining logic design with architecture. I recommend Harris’ book for this course.

Embedded Systems
Book (Perplexity): Embedded Systems: Introduction to the MSP432 Microcontroller by Jonathan W. Valvano.
Book (My Recommendation): Making Embedded Systems by Elecia White.
Verdict: Valvano’s book is better for ARM-specific systems, while White’s book is more general and practical. Use both depending on your goals.

Electrical Engineering
Book (Perplexity): Fundamentals of Electric Circuits by Charles K. Alexander and Matthew N.O. Sadiku.
Book (My Recommendation): The Art of Electronics by Paul Horowitz.
Verdict: Both are excellent. Alexander’s book is better for beginners, while Horowitz’s is better for engineers building real circuits. Include both.

Final Notes
Both lists complement each other well. I’ve added a few classes that Perplexity suggested (e.g., Linear Algebra, Cybersecurity, Quantum Computing) and refined some of the book/resources recommendations (e.g., Harris for hardware, Oppenheim + Ifeachor for DSP, etc.).

Let me know if you want a more customized learning path!"                                                                                             I forget to add networking and telecomiunications review the following book recommendations and choose between the two or offer a better recomendation for telecomunications I got recommended "Telecommunications: A Technical Primer" by A. Bruce Eisenberg, William A. Kiser, and John E. McDonald
and
"Wireless Communications: Principles and Practice" by Theodore S. Rappaportthat's by you from another conversation and by hugging face chat gpt 4 mini AI model and on the networking side it was "Computer Networking: A Top-Down Approach" by James Kurose and Keith Ross and
"Network Warrior" by Gary A. Donahue from same two AI models.damn I forgot again sorry while I was searching how to be an inventor like tony stark and book recommendations for mechatronics engineering  I got recommended these books :"How To Build A Robot: A Comprehensive Guide to Building a Robot" by J. P. Garmendia - This practical guide walks you through the steps to create your own robots and understand the technology behind them,"How to Create a Mind: The Secret of Human Thought Revealed" by Ray Kurzweil - This book explores the workings of the human brain and how they can be replicated in machines, perfect for those interested in AI and robotics,
"Robot Building for Beginners" by David Cook,"Introduction to Autonomous Robots" by Roland Siegwart, Illah Nourbakhsh, and Davide Scaramuzza,
"How to Diagnose and Fix Everything Electronic" by Michael Jay Geier
and finally "Introduction to Mechatronics and Measurement Systems" by David G. Alciatore and Michael B. Histand

I also plan on taking courses and exam prep on the comptia a+,secuirity and networking and the CCNA which are just the ones I know of  right know I'm sure there are lot more out there I hope I wrote them right and yep and that's  all what do you think feel free to add as many resources,classes,courses,subjects,etc as you like but don't forget the book and other resource recommendations like in this list all I want is a complete EECS curriculim it does not have to be solely based on the one from MIT I just want a couple things from this curriculim: 1)to study as hard and at the higher level as a EECS major from ivy league schools  but from home 2)a curriculm that has a secrete advantage to it,it has to have classes ,subjects,and book recommendations that will put me and others ahead of those involved in the field of EECS.3)to create a curriculum/roadmap not just for me but for future EECS majors 4)again be part of the 1% in electrical engineering and computer Science.