"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 Systems
  • GNSS
• Numerical Methods
  • Optimization Theory
• Signal Processing
  • Signals & Systems
  • Digital Signal Processing
  • Adaptive Filtering & Statistical Signal Processing
  • System identification

Algorithm Theory

• course book code CSE373 - Analysis of Algorithms - Taught by Prof. Steven Skiena. He covers topic such as data structure, searching and sorting algorithms, shortest-path algorithms, dynamic programming, and NP-Completeness.
• course CS106B - Programming Abstractions - 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.
• book code - Introduction to Algorithms. Thomas H. Cormen, Charles E. Leiserson, Ronald L - C Implementation of all the algorithms and data structures discussed in the textbook Introduction to Algorithms by Thomas H. Cormen, et al.
• reading Foundations of Computer Science - 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.
• video The hidden beauty of the A* algorithm.
• video How Dijkstra's Algorithm Works.
• video Understanding B-Trees: The Data Structure Behind Modern Databases
• code hello-algo - Data Structures and Algorithms Crash Course with Animated Illustrations and Off-the-Shelf Code.

Artificial Intelligence & Data Science

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

Machine Learning & Neural Networks

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

Linear Algebra

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

Communication Systems

• course book reading reading reading reading reading reading reading reading reading reading reading reading video video video video code - ECE4305 Software Defined Radio Systems and Analysis - 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.
• course reading reading reading code Software-Defined Radio Using MATLAB, Simulink, and the RTL-SDR - 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.
• course Software Defined Radio with HackRF - 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.
• course Signal Integrity: Optimizing RF Signal Chains - 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.
• reading 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 - By Fred Harris.
• code Software Radio for Experimenters with GNU Radio - Implemented in Octave and Python by Michel Barbeau.

GNSS

• reading GLONASS & GPS HW design.
• reading code - GNSS data processing with Python - Hands-on tutorials for GNSS data processing using Python and Jupyter Notebooks/book.
• video - GPS Spoofing With The HackRF On Windows
• code GPS Toolbox - 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

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

Optimization Theory

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

Signal Processing

Signals & Systems

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

Digital Signal Processing

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

Adaptive Filtering & Statistical Signal Processing

• course code MIT OpenCourseWare 18.065 - Matrix Methods in Data Analysis, Signal Processing, and Machine Learning.
• course reeading reading reading code - Mathworks course on Kalman filtering.
• book code Adaptive Filtering Algorithms and Practical Implementation - By Paulo S. R. Diniz.
• book code Adaptive Filter Theory - By Simon Haykin. 3th edition.
• book code Kalman and Bayesian Filters in Python - 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.
• code pyaec - A simple and efficient python implemention of a series of adaptive filters for acoustic echo cancellation.
• code Kernel Adaptive Filtering in Python - Implementation of LMS, RLS, KLMS and KRLS filters in Python.
• code Adaptive Filtering code of Matlab Adaptive Filtering toolbox - Repository containing a Python implemetation of the Matlab Adaptive Filtering toolbox.
• code Matlab codes for Statistical Signal Processing algorithms - 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 Code solution of three classical adaptive filter books - 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.
• code Collection of implementations of adaptive filters - Recursive Least Squares, Partial Least Squares, Moving Window Least Squares, Recursive Locally Weighted Partial Least Squares, Online Passive Aggressive Algorithm, Kalman Filter.

System identification

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