"The most vitally characteristic fact about Mathematics is, in my opinion, its quite peculiar relationship to the natural sciences, or, more generally, to any science which interprets experience on a higher than purely descriptive level."

John von Neumann.

An awesome list of academic resources for STEM (Science, Technology, Engineering, Mathematics) organized by subjects.

Contents

• Algorithm Theory
• Artificial Intelligence & Data Science
  • Machine Learning & Neural Networks
• Linear Algebra
• Communication & Navigation Systems
  • Satellite-based systems
• Numerical Methods
  • Optimization Theory
• Signal Processing
  • Signals & Systems
  • Digital Signal Processing
  • Adaptive Filtering & Statistical Signal Processing
  • System identification

Algorithm Theory

• CSE373 - Analysis of Algorithms course book code - Taught by Prof. Steven Skiena. He covers topic such as data structure, searching and sorting algorithms, shortest-path algorithms, dynamic programming, and NP-Completeness.
• CS106B - Programming Abstractions course - Stanford Engineering Everywhere: Object-oriented programming, fundamental data structures (such as stacks, queues, sets) and data-directed design. Recursion and recursive data structures (linked lists, trees, graphs). Introduction to time and space complexity analysis. It uses the C++ programming language covering its basic facilities.
• Introduction to Algorithms. Thomas H. Cormen, Charles E. Leiserson, Ronald L book code - - C Implementation of all the algorithms and data structures discussed in the textbook Introduction to Algorithms by Thomas H. Cormen, et al.
• Foundations of Computer Science reading - Course Notes for CSC110 and CSC111: Propositional Logic; Big-O, Omega, Theta; Data Types, Abstract and Concrete; Linked Lists; Induction and Recursion; Trees; Graphs; Sorting.
• The hidden beauty of the A* algorithm video
• How Dijkstra's Algorithm Works. video
• Understanding B-Trees: The Data Structure Behind Modern Databases video
• hello-algo code - Data Structures and Algorithms Crash Course with Animated Illustrations and Off-the-Shelf Code.

Artificial Intelligence & Data Science

• Applied Data Science with Python Specialization course - Gain new insights into your data. Learn to apply data science methods and techniques, and acquire analysis skills. University of Michigan. Coursera.
• Advanced Data Science with IBM Specialization course - Expert in Data Science, Machine Learning and AI. Become an IBM-approved Expert in Data Science, Machine Learning and Artificial Intelligence. Coursera.
• AI-For-Beginners reading - A 12 Weeks, 24 Lessons, AI for All.

Machine Learning & Neural Networks

• Convolutional Neural Networks course - A DeepLearningAI course on YouTube.
• Introduction to Machine Learning course - 10-315, Spring 2023. Carnegie Mellon University (CMU).
• Mathematical Foundations for Machine Learning course reading code - 10-606, Fall 2022. Carnegie Mellon University (CMU).
• Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control book code - By Steven L. Brunton and J. Nathan Kutz. 1th edition.
• Mathematics for Machine Learning Book book reading code - by A. Aldo Faisal, Cheng Soon Ong, and Marc Peter Deisenroth.
• Neural Networks and Learning Machines book reading code code code - By Simon Haykin. 3th edition.
• Creating Deep Learning Models Using Keras. video - Deep Learning, Simplilearn.
• Building a neural network from scratch. video
• How convolutional neural networks work, in depth. video
• MIT 6.S191 (2022): Convolutional Neural Networks. video
• Bias Variance trade-off. video
• generative-models code - Collection of generative models, e.g. GAN, VAE in PyTorch and TensorFlow.
• LLMs-from-scratch code - Implementing a ChatGPT-like LLM from scratch, step by step.

Linear Algebra

• MIT 18.06, Linear Algebra course code - By Professor Gilbert Strang.
• Abstract Algebra course - A YouTube course from Socratica.
• Introduction to Linear Algebra book solution reading code - Gilbert Strang. 5th edition.
• The Art of Linear Algebra reading - Linear Algebra course by Professor Gilbert Strang.
• What is Jacobian? video - Multivariable calculus: The right way of thinking derivatives and integrals.

Communication & Navigation Systems

• ECE4305 Software Defined Radio Systems and Analysis course book reading reading reading reading reading reading reading reading reading reading reading reading video video video video code - - Hands-on course on SDR communication system from the Department of Electrical and Computer Engineering (ECE), at Worcester Polytechnic Institute (WPI), taught by Prof. Dr. Alexander Wyglinski. The course is based on the book "Software-Defined Radio for Engineers", developed by Analog Devices Inc. (ADI) and written by Travis F. Collins et. al., and uses ADALM-PLUTO SDR, also manufactured by ADI.
• Software-Defined Radio Using MATLAB, Simulink, and the RTL-SDR course reading reading reading code - A hands-on course on SDR using the Realtek RTL2832U chip. It is based on the book "Software Defined Radio Using MATLAB & Simulink and the RTL-SDR", by Bob Stewart. It uses the RTL-SDR device, emerged from the repurposing of digital TV (DVB-T) USB dongles that use the RTL2832U chipset, which was originally developed by Realtek Semiconductor Corp.
• Software Defined Radio with HackRF course - Video series of a complete course in Software Defined Radio (SDR). This course builds flexible SDR applications using GNU Radio through exercises that will help you learn the fundamentals of Digital Signal Processing (DSP) needed to master SDR. For the over-the-air exercises, you’ll need a HackRF One or other SDR peripheral.
• Signal Integrity: Optimizing RF Signal Chains course - A single and long webinar provided by Analog Devices Inc. (ADI). It covers many technical and theoretical aspects of radiofrequency front-end (RF-FE) signal chains. It also mentions ADALM-PLUTO SDR, a software-defined radio deviced manufactured by this company and extensively used for learning purposes by hobbists and students.
• Book Quadrature Signals: Complex, But Not Complicated. reading
• How I learned to love the trellis. reading
• I/Q Data for Dummies. reading
• Let's Assume the System is Synchronized reading - By Fred Harris.
• Software Radio for Experimenters with GNU Radio code - Implemented in Octave and Python by Michel Barbeau.

Satellite-based systems

• GLONASS & GPS HW design. reading
• GNSS data processing with Python reading code - Hands-on tutorials for GNSS data processing using Python and Jupyter Notebooks/book.
• GPS Spoofing With The HackRF On Windows video - GPS spoofing demonstration using HackRF.
• GPS Toolbox code - GPS Toolbox topical collection of the journal GPS Solutions. It provides a means for distributing the source code and algorithms discussed in the GPS Toolbox topical collection.

Numerical Methods

• Engineering Math: Differential Equations and Dynamical Systems course - By Steve Brunton. Introduction and overview to Differential Equations & Dynamical Systems. Dynamical systems are differential equations that describe any system that changes in time.
• Numerical Methods for Engineers book code code - By Steven C. Chapra and Raymond P. Canale. 7th edition.

Optimization Theory

• EE364A, Convex Optimization I course book reading reading code - Stanford Engineering Everywhere - Stephen Boyd.
• EE364b - Convex Optimization II course - Stanford Engineering Everywhere - Stephen Boyd.
• CVX101 Stanford course code code - StanfordOnline: Convex Optimization.
• Convex Optimization book solution reading - Boyd, S.P. and Vandenberghe, L., 2004. Cambridge university press.
• Convex Optimization video - An intuitive explanation of convex optimization and how it works, from the YouTube channel Visually Explained.
• Optimization Problem Types. reading
• DCP analyzer. - reading

Signal Processing

Signals & Systems

• MIT OpenCourseWare in Signals And Systems course - An introduction to analog and digital signal processing.

Digital Signal Processing

• MIT OpenCourseWare in Discrete-Time Signal Processing course - It addresses the representation, analysis, and design of discrete time signals and systems.
• Advanced Signal Processing Notebooks and Tutorials course - By Prof. Dr. -Ing. Gerald Schuller, Applied Media Systems Group, Technische Universität Ilmenau.
• Discrete-Time Signal Processing book solution - By Alan V. Oppenheim and Ronald W. Schafer. 3th edition. Prentice Hall Signal Processing.

Adaptive Filtering & Statistical Signal Processing

• MIT OpenCourseWare 18.065 course code - Matrix Methods in Data Analysis, Signal Processing, and Machine Learning.
• Mathworks course on Kalman filtering course reading reading reading code - Comprehensive training on Kalman filter design and implementation.
• Adaptive Filtering Algorithms and Practical Implementation book code - By Paulo S. R. Diniz.
• Adaptive Filter Theory book code - By Simon Haykin. 3th edition.
• Kalman and Bayesian Filters in Python book code - Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more.
• pyaec code - A simple and efficient python implemention of a series of adaptive filters for acoustic echo cancellation.
• Kernel Adaptive Filtering in Python code - Implementation of LMS, RLS, KLMS and KRLS filters in Python.
• Adaptive Filtering code of Matlab Adaptive Filtering toolbox code - Repository containing a Python implemetation of the Matlab Adaptive Filtering toolbox.
• Matlab codes for Statistical Signal Processing algorithms code - Matlab code implementing different methods used in statistical signal processing; mainly Extended Kalman Filters, LMS/RLS, Wiener, robust regression, MMSE estimators, ML estimators, Hi-Frequency estimators (Pisarenko, MUSIC, ESPRIT).
• Code solution of three classical adaptive filter books code - Adaptive Filter Theory (5th Edition) wrotten by Simon Haykin, Adatpive Filtering: Algorithms and Practical Implentation (4th Edition) wrotten by Paulo S R. Diniz, and Adaptive Filters: Theory and Application (2nd Edition) wrotten by Behrouz Farhang-Boroujeny.
• Collection of implementations of adaptive filters code - Recursive Least Squares, Partial Least Squares, Moving Window Least Squares, Recursive Locally Weighted Partial Least Squares, Online Passive Aggressive Algorithm, Kalman Filter.

System identification

• ECE5560: System Identification course - A course on System Identification from the University of Colorado, taught by Prof. Dr. Gregory L. Plett.
• MathWorks course on System Identification course video - Taught by Lennart Ljung, Linköping University.