Contents
Home
AI Timeline AI Landscape Guide to Major Papers Modern Reading Guide
Compendium
All parts & chapters →
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
About

Hi, I’m Alex.

I came to AI the long way around. I went to culinary school at the Culinary Institute of America, fell hard for wine, and spent a decade and a half there because wine turned out to be many lenses on the world compressed into one glass — geography, geology, climate, agriculture, history, culture, economics — each bottle a small argument for why all of those matter at once. There, I became a wines tutor. That part mattered more than it sounds: the tutor job is closer to a university recitation than a lecture — students come to you after class to digest what they just heard — and I found that building worksheets, writing outlines, and quizzing people relentlessly (and drawing many, many bad maps) was the fastest way to force my own understanding into shape. I taught about 150 students that way before I passed the Certified Sommelier Exam and graduated. I went on to work the floor at Restaurant August in New Orleans, Element 47 at The Little Nell in Aspen, Frasca Food & Wine in Boulder, and Matsuhisa in Denver, and along the way I tutored a dozen more candidates for the Certified, passed the Advanced Sommelier Exam myself, and helped several more people prepare for the Advanced.

Alex in a straw fedora standing between rows of Sangiovese vines in a Chianti Classico vineyard, with Tuscan hills and a stone village in the background.
The Conca d'Oro in Chianti Classico. Many hills, one grape, and a solid argument for why place matters.

ChatGPT 3.5 was the moment I pivoted. It was clear this was going to reshape the working world, and I wanted to build things inside of it rather than watch from outside. I brushed up on my math (realizing I might have been a Math major had I taken Discrete earlier), completed a Master’s in Data Science at the University of Denver, and I’m now preparing to move into AI full time.

This site is my working compendium — walkthroughs, reading guides, and maps of the field as I currently understand it. Publishing them is my honesty mechanism; prose that has to stand up to a stranger has to actually hold together. And in the spirit of honesty: Claude has been my closest collaborator on this website, and I’m in a constant back-and-forth with the model to make sure the content and the form match my standards.

Email is the best way to reach me: alexholyk@gmail.com. Outside of work I ride bikes (it isn’t serious enough to say I cycle) and read everything I can get my hands on. Currently that’s the Dungeon Crawler Carl series.

— Alex Holyk

The Collection
Interactive Timeline Landscape Map Papers Guide Reading Guide