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Compendium
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IMathematical Foundations
01Linear Algebra 02Calculus & Differential Equations 03Optimization Theory 04Probability Theory 05Statistics & Statistical Inference 06Information Theory 07Bayesian Reasoning 08Signal Processing
IIProgramming & Software Engineering
01Python for Data Science 02Scientific Computing 03Algorithms & Data Structures 04Software Engineering Principles 05Databases & SQL 06Version Control & Collaborative Development
IIIData Engineering & Systems
01Data Collection & Acquisition 02Data Storage & Warehousing 03Data Pipelines & Orchestration 04Streaming & Real-Time Data 05Distributed Computing 06Cloud Platforms & Infrastructure 07Data Quality, Governance, & Metadata
IVClassical Machine Learning
01Supervised Learning: Regression 02Supervised Learning: Classification 03Ensemble Methods 04Unsupervised Learning: Clustering 05Dimensionality Reduction 06Probabilistic Graphical Models 07Kernel Methods & SVMs 08Feature Engineering & Selection 09Model Evaluation & Selection
VDeep Learning Foundations
01Neural Network Fundamentals 02Training Deep Networks 03Regularization & Generalization 04Convolutional Neural Networks 05Sequence Models 06Attention Mechanisms 07Transfer Learning & Pretraining
VINLP & Large Language Models
01NLP Fundamentals 02Classical NLP 03Word Embeddings & Distributional Semantics 04The Transformer Architecture 05Pretraining Paradigms 06LLMs: Scale & Emergent Capabilities 07Instruction Tuning & Alignment 08Fine-Tuning & Parameter-Efficient Adaptation 09Retrieval-Augmented Generation 10LLM Evaluation
VIIComputer Vision
01Image Representation & Classical Vision 02Modern Image Classification & Architectures 03Object Detection & Instance Segmentation 04Video Understanding 053D Vision & Spatial Understanding 06Vision-Language Models
VIIISpeech, Audio & Music
01Audio Signal Processing 02Automatic Speech Recognition 03Text-to-Speech & Voice Synthesis 04Speaker Recognition & Diarization 05Audio Classification & Sound Understanding 06Music Generation & Music AI
IXReinforcement Learning
01RL Fundamentals 02Tabular RL 03Deep Q-Networks & Value-Based Methods 04Policy Gradient & Actor-Critic Methods 05Model-Based RL & World Models 06Multi-Agent Reinforcement Learning 07Offline RL & Imitation Learning 08Preference Learning & RLHF
XGenerative Models
01Variational Autoencoders 02Generative Adversarial Networks 03Normalizing Flows 04Diffusion Models 05Autoregressive Generative Models 06Image & Video Generation 073D & Multimodal Generation 08Multimodal Foundation Models
XIAI Agents & Autonomous Systems
01Agent Fundamentals 02LLM-Based Agents 03Tool Use & Function Calling 04Memory & Knowledge Management 05Planning & Reasoning 06Multi-Agent Systems 07Agent Evaluation & Benchmarking
XIIRobotics & Embodied AI
01Robot Perception & Sensing 02Motion Planning & Control 03Learning from Demonstration & Imitation 04Sim-to-Real Transfer 05Foundation Models for Robotics 06Autonomous Vehicles
XIIISpecialized ML Methods
01Time Series Analysis & Forecasting 02Anomaly Detection 03Causal Inference 04Causal Machine Learning 05Graph Neural Networks 06Survival Analysis & Event Modeling 07Bayesian Deep Learning 08Meta-Learning & Few-Shot Learning 09Continual & Lifelong Learning 10Federated Learning & Privacy-Preserving ML 11Neurosymbolic AI
XIVApplied Domains
01Recommender Systems 02Search & Information Retrieval 03Financial ML & Quantitative Methods 04Healthcare & Clinical AI 05AI for Cybersecurity 06AI for Education & Personalization 07AI for Manufacturing & Operations 08Human-AI Interaction & UX
XVAI for Science
01Scientific Machine Learning 02AI for Biology & Genomics 03AI for Drug Discovery & Molecular Design 04AI for Protein Science 05AI for Climate & Earth Systems 06AI for Physics, Materials & Astronomy
XVIMLOps & Production ML
01Experiment Tracking & Reproducibility 02Feature Stores & Data Management for ML 03Model Deployment & Serving 04Model Monitoring & Drift Detection 05CI/CD for Machine Learning 06A/B Testing & Causal Experimentation 07Responsible Release & Deployment Practices
XVIIAI Infrastructure & Systems
01Hardware for ML 02Distributed Training 03Model Compression 04Inference Optimization 05AI Chips & Custom Silicon
XVIIIAI Safety, Alignment & Governance
01AI Safety Fundamentals 02Technical Alignment Methods 03Robustness & Adversarial ML 04Mechanistic Interpretability 05Explainability for Practitioners 06Fairness, Bias & Equity 07Privacy in ML 08AI Governance, Policy & Regulation
Chapter 02 · Landscape

An Interactive Map of the AI & Data Science Landscape

Each labelled region is a thematic cluster; each rectangle is a live sub-field of research and practice. Click any sub-field to see what it focuses on, where it's used, which technologies matter, and the best places to start learning about it.