Learning Resources

A curated, growing collection of freely available books, video courses and websites for learning the tools we use in the group — from first steps in Python to model predictive control, reinforcement learning and Bayesian optimisation. Everything linked here is free to access.

🐍 Python 🌀 Dynamic Modelling 📈 Optimisation & Design Opt. 🎛️ Model Predictive Control 🤖 Reinforcement Learning 🎯 Bayesian Optimisation

🐍

Python

Start here if you're new to programming — no prior experience needed.

Books

Automate the Boring Stuff with PythonAl Sweigart — practical, beginner-friendly, read free online
Python for EverybodyCharles Severance — gentle intro to programming
Think Python (2e)Allen B. Downey — thinking like a computer scientist
A Byte of PythonSwaroop C. H. — concise first introduction

YouTube

Python Full Course for BeginnersfreeCodeCamp — 4.5 hour complete intro
Python TutorialsCorey Schafer — clear, topic-by-topic playlist
Python for EverybodyDr. Chuck (Charles Severance) — full course playlist
Python Programmingsentdex — hands-on, project-based

Websites

The Official Python Tutorialpython.org — the authoritative starting point
Kaggle: Learn PythonFree hands-on micro-course with exercises
Real PythonWell-written tutorials on every topic
Scientific Python LecturesNumPy, SciPy, Matplotlib for science & engineering

🌀

Dynamic Modelling

Differential equations, dynamical systems and data-driven models.

Books

Data-Driven Science & EngineeringBrunton & Kutz — free PDF + code + videos
Differential Equations for EngineersJeffrey R. Chasnov — free textbook (HKUST)
Process Dynamics & ControlAPMonitor — course notes, code and examples

YouTube

Nonlinear Dynamics & ChaosSteve Brunton — accessible, visual playlist
Engineering Math: Differential Equations & Dynamical SystemsSteve Brunton
Process Dynamics & ControlJohn Hedengren (APMonitor)

Websites

APMonitor — Process Dynamics & ControlInteractive lessons with Python (GEKKO)
PySINDyDiscover dynamical models from data
SciPy: Integrating ODEssolve_ivp tutorial and reference

📈

Optimisation & Design Optimisation

From convex optimisation foundations to engineering design (MDO).

Books

Engineering Design OptimizationMartins & Ning — free PDF, ideal for design opt.
Convex OptimizationBoyd & Vandenberghe — free PDF, the classic
Algorithms for OptimizationKochenderfer & Wheeler (MIT) — free PDF

YouTube

Convex Optimization (EE364a)Stephen Boyd — full Stanford lecture course
OptimizationSteve Brunton — gradient methods, constraints and more
Multidisciplinary Design OptimizationJoaquim Martins — companion to the MDO book

Websites

PyomoPython modelling for optimisation problems
CVXPYConvex optimisation, embedded in Python
SciPy OptimizePractical solvers for everyday problems
NEOS Optimization GuideReference on optimisation problem types

🎛️

Model Predictive Control

Optimisation-based control — theory, computation and implementation.

Books

Model Predictive Control: Theory, Computation & DesignRawlings, Mayne & Diehl — free PDF (2nd ed.)
Predictive Control for Linear & Hybrid SystemsBorrelli, Bemporad & Morari — draft + slides

YouTube

Understanding Model Predictive ControlMATLAB Tech Talks — clear, intuitive intro
Model Predictive ControlSteve Brunton — concise overview
Model Predictive ControlJohn Hedengren (APMonitor) — hands-on with Python

Websites

do-mpcPython toolbox for robust & nonlinear MPC
CasADiSymbolic framework for optimal control
GEKKOPython for dynamic optimisation & MPC

🤖

Reinforcement Learning

Learning to make decisions through interaction and reward.

Books

Reinforcement Learning: An IntroductionSutton & Barto — the standard text, free PDF
Spinning Up in Deep RLOpenAI — free, code-first guide to deep RL

YouTube

Introduction to Reinforcement LearningDavid Silver (DeepMind) — the classic course
DeepMind x UCL RL Lecture SeriesDeepMind & UCL — modern, thorough
Reinforcement LearningSteve Brunton — engineering perspective

Websites

Hugging Face Deep RL CourseFree, hands-on, with certificates
GymnasiumStandard API & environments for RL
Stable-Baselines3Reliable RL algorithm implementations

🎯

Bayesian Optimisation

Sample-efficient optimisation of expensive black-box functions.

Books

Bayesian OptimizationRoman Garnett — free PDF, the definitive text
Gaussian Processes for Machine LearningRasmussen & Williams — free PDF (GP foundations)

YouTube

Machine Learning - Bayesian OptimizationNando de Freitas — clear lecture
Gaussian Process & Bayesian OptimizationIntroductory tutorial talk

Websites

Exploring Bayesian OptimizationDistill.pub — beautiful visual explainer
BoTorchBayesian optimisation in PyTorch
GPyTorchScalable Gaussian processes
AxAdaptive experimentation platform (Meta)

Know a great free resource we should add? Get in touch — we'd love to grow this list.