How to Use the Maths, CS & AI Compendium for Interview Preparation: A Complete Guide

The Maths, CS & AI Compendium is a structured open-source textbook that combines mathematical foundations, computer science fundamentals, and AI concepts, making it ideal for systematic technical interview preparation through its curriculum-based organization and hands-on code examples.

The Maths, CS & AI Compendium by HenryNdubuaku is an open-source, self-contained textbook that merges mathematical foundations, core computer science concepts, and modern AI topics into a single resource. Whether you are preparing for software engineering, machine learning engineering, or research scientist roles, this repository provides a systematic approach to mastering the technical concepts interviewers expect. The compendium's modular structure covers everything from linear algebra to GPU programming, allowing you to target specific knowledge gaps efficiently.

Curriculum-Level Organization

The repository's README.md maps out 18 top-level chapters that span the full technical interview spectrum, from fundamental mathematics to advanced ML systems design. According to the source code at lines 18-38 of the README, these chapters include Vectors, Matrices, Calculus, Statistics, Probability, Machine Learning, Computational Linguistics, Computer Vision, Audio & Speech, Multimodal Learning, Autonomous Systems, Graph Neural Networks, Computing & OS, Data Structures & Algorithms, Production Software Engineering, SIMD & GPU Programming, AI Inference, and ML Systems Design.

Each chapter contains markdown files that walk through theory, intuition, and practical examples. This structure allows you to target specific domains that appear in your target role's interviews. For example, candidates for ML engineering positions should focus on chapter 06: machine learning/01. classical machine learning.md for core concepts like bias-variance tradeoff and regularization, while software engineering candidates should prioritize chapter 14: data structures and algorithms/00. foundations.md for Big-O analysis and graph algorithms.

The Two-Phase Study Workflow

The compendium recommends a specific study methodology that mirrors the "read-recall-apply" loop interviewers expect. As documented in README.md at lines 55-60, the workflow divides your preparation into two distinct phases:

1. Phase 1: Cumulative Reading – Read each concept immediately after learning it in your coursework or self-study routine. This reinforces the material while it is fresh and builds a foundation of understanding.

2. Phase 2: Shadow Reading – Before exams or interviews, close the text and visualize the concept. Write a concise explanation from memory, then implement a small code snippet to verify your understanding. This active recall technique strengthens neural pathways and prepares you for whiteboard-style interviews where you must explain concepts without references.

Hands-On Code Integration

Interview preparation requires more than theoretical knowledge. The compendium embeds practical usage examples throughout its chapters, including runnable Python snippets that you can copy into local notebooks for experimentation.

For example, the Calculus chapter includes a gradient descent implementation that demonstrates optimization fundamentals frequently discussed in ML interviews:

# Example: Gradient Descent for a simple quadratic (from Chapter 03 – Calculus)

import numpy as np

def grad_descent(f_prime, init, lr=0.1, steps=100):
    x = init
    for _ in range(steps):
        x -= lr * f_prime(x)
    return x

# f(x) = (x-3)^2  ->  f'(x) = 2*(x-3)

root = grad_descent(lambda x: 2*(x-3), init=0.0)
print("Estimated minimum at:", root)   # -> 3.0

Similarly, chapter 17: AI inference/01. quantisation.md covers model quantization techniques increasingly asked in system-design rounds, while the SIMD & GPU Programming chapter includes CUDA kernel examples for high-performance computing interviews.

AI-Assisted Preparation with the MCP Server

The repository includes an MCP (Model Context Protocol) server that enables AI-assisted querying of the compendium content. Located in the mcp_server/ directory, this tool allows you to spin up a local REST API that AI assistants like Claude Code, Cursor, or VS Code can query programmatically.

To start the server locally:

# Running the MCP server (requires a local clone)

git clone https://github.com/HenryNdubuaku/maths-cs-ai-compendium.git
cd maths-cs-ai-compendium
python -m mcp_server   # starts a local REST API

Once running, you can query specific concepts for rapid recall during mock interviews:

# Query the MCP server for a quick definition

GET http://localhost:8000/query?text=What+is+backpropagation%3F

This is especially useful for last-minute review or when you need targeted explanations of complex topics like transformer architectures or graph neural network propagation without manually searching through files.

Citation-Ready Reference

The repository includes a ready-made BibTeX entry for academic or professional citations, located at lines 67-75 of the README.md. This allows you to reference the compendium in your résumé, LinkedIn, or personal portfolio, demonstrating your commitment to foundational technical knowledge in mathematics, computer science, and artificial intelligence.

Summary

• Curriculum Coverage: The Maths, CS & AI Compendium organizes 18 chapters spanning vectors to ML systems design in README.md, making it easy to target specific interview topics.
• Active Learning: The two-phase workflow (cumulative reading followed by shadow reading) builds the recall and application skills necessary for technical interviews.
• Practical Implementation: Runnable code examples in chapters like Calculus and AI Inference let you verify understanding through implementation.
• AI Integration: The built-in MCP server enables rapid querying of concepts during study sessions or mock interviews.
• Professional Credibility: Built-in BibTeX citation support allows you to add the compendium to your professional references.

Frequently Asked Questions

How do I know which chapters to focus on for my specific role?

Focus on chapters that align with your target position's requirements. For machine learning engineering roles, prioritize chapter 06: machine learning/01. classical machine learning.md for algorithms and bias-variance tradeoffs, and chapter 17: AI inference/01. quantisation.md for deployment concepts. For software engineering positions, concentrate on chapter 14: data structures and algorithms/00. foundations.md for Big-O complexity and graph algorithms, and chapter 15: production software engineering for system design principles.

Can I use the MCP server during actual interviews?

The MCP server is designed for interview preparation and study sessions, not for use during actual interviews. Running the server locally allows you to practice explaining concepts and receiving AI-generated explanations during mock interviews, but you should rely on your own knowledge during real technical assessments. The server helps you internalize concepts through repetition and varied explanations.

What is the best way to use the code examples for learning?

Copy the Python snippets from relevant chapters into a local Jupyter notebook or IDE. Modify the parameters, break the code intentionally to see error messages, and try to reimplement the algorithms from memory after studying the examples. For instance, after reviewing the gradient descent example in the Calculus chapter, attempt to write your own implementation for a different loss function without looking at the reference code.

Is the compendium suitable for beginners in AI and CS?

Yes, the compendium structure supports progressive learning. Beginners should start with foundational chapters like Vectors, Matrices, and Calculus, then move to Statistics and Probability before tackling Machine Learning. The two-phase study method specifically supports newcomers by emphasizing immediate reinforcement of new concepts followed by active recall sessions, ensuring foundational knowledge is solid before advancing to complex topics like Graph Neural Networks or Autonomous Systems.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}