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

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. Uses the programming language C++ covering its basic facilities.
• 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 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

• 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.
• 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.

Optimization Theory

• course reading reading 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.
• reading Optimization Problem Types.
• reading DCP analyzer.

Numerical Methods

• book code code Numerical Methods for Engineers - By Steven C. Chapra and Raymond P. Canale. 7th edition.

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.
• 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.