Reading List 📚

Systems & Design Thinking

1. "Thinking in Systems: A Primer" by Donella H. Meadows - Introduction to systems thinking.
2. "The Fifth Discipline: The Art & Practice of The Learning Organization" by Peter Senge - Organizational learning and systems thinking.
3. "Design Thinking: Integrating Innovation, Customer Experience, and Brand Value" by Thomas Lockwood - How design thinking integrates into various areas.
4. "Introduction to Systems Thinking" by Barry Richmond - A beginner's guide to systems thinking.
5. "The Design of Everyday Things" by Don Norman - User-centric design principles.

Modelling & Simulation

1. "System Dynamics: Modeling, Simulation, and Control of Mechatronic Systems" by Dean C. Karnopp - Comprehensive overview of system dynamics.
2. "Modeling and Simulation of Dynamic Systems" by Robert L. Woods and Kent L. Lawrence - Basics of modeling physical systems.
3. "Simulation Modelling and Analysis" by Averill M. Law - Advanced concepts in simulation and analysis.
4. "Guide to Discrete Event Simulation" by Paul A. Fishwick - An introduction to discrete event simulation.
5. "Business Dynamics: Systems Thinking and Modeling for a Complex World" by John Sterman - How modeling can be used to address complex business challenges.

Tools & Techniques

1. "The Systems Thinker's Toolkit" by Truby Chiaviello - A collection of tools for systems thinking.
2. "Tools of Systems Thinkers" by Albert Rutherford - Techniques for applying systems thinking in various fields.
3. "Agile Estimating and Planning" by Mike Cohn - Using agile methods in planning and estimating.
4. "The Art of Systems Architecting" by Mark W. Maier & Eberhardt Rechtin - Principles and practices of systems architecture.
5. "Lean Software Development: An Agile Toolkit" by Mary Poppendieck - Tools and techniques for implementing lean principles in software development.

AI & ML

1. "Artificial Intelligence: A Guide to Intelligent Systems" by Michael Negnevitsky - Introduction to AI concepts and technologies.
2. "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - Comprehensive book on deep learning.
3. "Machine Learning: A Probabilistic Perspective" by Kevin P. Murphy - Focus on probabilistic models in machine learning.
4. "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto - A standard text on reinforcement learning.
5. "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron - A practical guide to implementing machine learning models using popular libraries.

Twitter accounts to follow

Coming soon!