Here is our selection for the computer science library and math

*Deep learning and neural networks*

Mathematics for Machine Learning

**Linear Algebra and Optimization for Machine Learning****Artificial Intelligence for Humans, Volume 1: Fundamental Algorithms**

**Artificial Intelligence for Humans, Volume 2: Nature-Inspired Algorithms**

**Artificial Intelligence for Humans, Volume 3: Deep Learning and Neural Networks**

**Clever Algorithms: Nature-Inspired Programming Recipes**

**Approaching (Almost) Any Machine Learning Problem**

**The Hundred-Page Machine Learning Book**

**Mathematics for Machine Learning**

**Artificial Intelligence: A Modern Approach, Global Edition**

*Programming language :*

**Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow**

**Julia Programming for Operations Research**

**Dynamic Programming for Coding Interviews**

*Programming practices:*

**Clean Architecture: A Craftsman's Guide to Software Structure and Design**

*Computational theories:*

**Introduction to Automata Theory, Languages, and Computation**

**Theory of Games and Economic Behavior**

**Game Theory 101: The Complete Textbook**

*Algorithmic:*

**Algorithms Illuminated: Part 1: The Basics**

**Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures**

**Algorithms Illuminated (Part 3): Greedy Algorithms and Dynamic Programming**

**Algorithms Illuminated (Part 4): Algorithms for NP-Hard Problems**

*Operational research and optimization:*

**Julia Programming for Operations Research**

**Combinatorial Optimization: Algorithms and Complexity**

**Optimization Techniques in Operation Research**

**Optimization Techniques in Operation Research**

*Decision theory:*

**Mathematics for Science - Concepts, Methods and Techniques for Modeling**

**An Introduction to Decision Theory**

**Theory of Decision under Uncertainty**

**Decision Theory: Principles and Approaches**

*Stochastic:*

**Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues**

**Markov chains - Corrected lessons and exercises**

**Introduction to Probability Models**

*Logic :*