When one engages in learning, does it actually show up in the outcomes? There’s often a joke about majoring in “Underwater Basket Weaving” and “Philosophy” leading towards a lifelong career as a hometown bookseller, which all things considered sans the salary, is not a bad gig in my humble opinion (and Philosophy has recently emerged as a potential critical skill in AI). Since finishing graduate school the first time, I have spent a bit over a decade engaged in naive self-learning via MOOCs and reading. Recently tabulating the total cost of all these activities, I’ve realized I probably could have gotten at least a mid-tier Tesla (with many trimmings) with that capital. Was it worth it?
Est. 2013 · still enrolling
There are 174 credentials here, from 8 platforms, and every one links to its own verification. The braid shows the shape of the record, seven threads pulled since 2013 and converging on the work this site reports.
The record is also becoming raw material for a side project, a custom sparring agent built to press on what one has learned, keep it sharp, and deepen it across past, current and future study. The motive behind it is the question of how humans best learn, perform, and create in the age of AI.
Every dot is one credential; click it to open the verification. Bars count credentials per year. 2022 is empty, and the record keeps its gaps; the dashed lane below the braid may be part of why: formal coursework at Georgia Tech that began with a few courses in 2022 and has accelerated since 2023. Dashed means declared, not yet documented. It runs under everything currently being built, so it is drawn into every output in that column, and reaches Formal Labs through them. Two of those edges are solid rather than dashed: the SAT reasoning paper and the QNLPAI poster came directly out of Georgia Tech coursework and research, so that lineage is known even while the course list behind it is not yet published here. That lane, and the dashed edges it sends forward, fill in when the data does. Ringed dots fed one workshop project by name; its sibling takes no course tags at all, on purpose. Thread assignment is editorial: ruled first, corrected by hand, still under revision.
Mathematics and statistics38 credentials · 2014 to 2024surfaces at CMSA, Cambridge ICM, Philadelphia
Commentary to come. This thread is data first, essay second.
- 2014-01Introduction to Complexity
Santa Fe Institute / Complexity Explorer - 2014-03Introduction to Dynamical Systems and Chaos
Santa Fe Institute / Complexity Explorer - 2014-06LAFF: Linear Algebra – Foundations to Frontiers (with Python)
edX - 2014-08Experimentation for Improvement
Coursera - 2014-10Solid Science: Research Methods→ F*********s, the method
Coursera - 2014-11An Introduction to Functional Analysis
Coursera - 2014-11Data Analysis and Statistical Inference
Coursera - 2014-12Linear and Integer Programming→ F*********s
Coursera - 2015-01Mathematical Biostatistics Boot Camp 1
Coursera - 2015-03Mathematical Biostatistics Boot Camp 2
Coursera - 2015-05Probability
Coursera - 2015-06Reproducible Research→ F*********s, the method
Coursera - 2015-06Statistical Thermodynamics: Molecules to Machines
Coursera - 2015-10Regression Models
Coursera - 2016-11Advanced Linear Models for Data Science 1: Least Squares
Coursera - 2016-12Advanced Linear Models for Data Science 2: Statistical Linear Models
Coursera - 2016-12Computational Probability and Inference
edX - 2017-02Observation theory: Estimating the Unknown
edX - 2017-05Improving your statistical inferences→ F*********s, the method
Eindhoven University of Technology - 2017-05009x: Stochastic Processes: Data Analysis and Computer Simulation
edX - 2017-10Measuring Causal Effects in the Social Sciences→ F*********s, the method
University of Copenhagen - 2017-12Bayesian Statistics: From Concept to Data Analysis
University of California, Santa Cruz - 2017-12ISYE6414x: Regression Analysis
edX - 2018-03Practical Time Series Analysis
The State University of New York - 2018-05PH527x: Principles, Statistical and Computational Tools for Reproducible Science
edX - 2018-06FC1x: Fat Chance: Probability from the Ground Up
edX - 2018-08ISYE6644x: Simulation and Modeling for Engineering and Science
edX - 2018-12ISYE6669: FA18: Deterministic Optimization→ F*********s
edX - 2019-01STAT110x: Introduction to Probability
edX - 2019-04PH559x: Causal Diagrams: Draw Your Assumptions Before Your Conclusions→ F*********s, the method
edX - 2019-06DAT273x: Data Science Research Methods: Python Edition
edX - 2019-0618.6501x: Fundamentals of Statistics
edX - 2020-04Principal Component Analysis with NumPy
Coursera - 2020-07Advanced Linear Algebra: Foundations to Frontiers
edX - 2021-02Introduction to Probability Management
edX - 2021-03Bayesian Optimization with Python
Deprecated Guided Projects - 2021-066.419x: Data Analysis: Statistical Modeling and Computation in Applications
edX - 2024-01Mathematical understanding of uncertainty
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Logic and formal methods5 credentials · 2014 to 2023surfaces at Miami Cambridge
Commentary to come. This thread is data first, essay second.
- 2014-03Games without Chance: Combinatorial Game Theory→ F*********s
Coursera - 2014-04Logic: Language and Information 1→ F*********s
Coursera - 2014-06Logic: Language and Information 2→ F*********s
Coursera - 2015-05Information Theory→ F*********s
Coursera - 2023-12Understanding Artificial Intelligence through Algorithmic Information Theory→ F*********s
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Machine learning46 credentials · 2014 to 2025surfaces at GTC, San Jose CVPR, Nashville SciFM, Ann Arbor
Commentary to come. This thread is data first, essay second.
- 2014-03Image and video processing: From Mars to Hollywood with a stop at the hospital
Coursera - 2014-12Learning From Data (introductory Machine Learning course) – In Python
edX - 2015-08Scalable Machine Learning with PySpark
edX - 2015-10Practical Machine Learning
Coursera - 2017-03Machine Learning with Big Data
Coursera - 2017-11Neural Networks and Deep Learning→ F*********s
DeepLearning.AI - 2017-11Structuring Machine Learning Projects
DeepLearning.AI - 2017-11Machine Learning Foundations: A Case Study Approach
University of Washington - 2017-12Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization
DeepLearning.AI - 2017-12Machine Learning: Regression
University of Washington - 2018-01Machine Learning: Classification
University of Washington - 2018-05Mathematics for Machine Learning: Linear Algebra
Imperial College London - 2018-08Mathematics for Machine Learning: Multivariate Calculus
Imperial College London - 2018-09Mathematics for Machine Learning
Imperial College London - 2018-09Mathematics for Machine Learning: PCA
Imperial College London - 2019-04Neural Networks for Machine Learning
University of Toronto - 2019-05Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
DeepLearning.AI - 2019-05CB0103EN: How to Build Chatbots and Make Money
edX - 2019-096.86x: Machine Learning with Python-From Linear Models to Deep Learning
edX - 2020-03Introduction to AWS DeepLens
LinkedIn Learning - 2020-04Support Vector Machines with scikit-learn
Deprecated Guided Projects - 2020-04Predict Future Product Prices Using Facebook Prophet
Deprecated Guided Projects - 2020-04Simple Recurrent Neural Network with Keras
Deprecated Guided Projects - 2020-04Perform Sentiment Analysis with scikit-learn
Deprecated Guided Projects - 2020-04Language Classification with Naive Bayes in Python
Deprecated Guided Projects - 2020-04Hierarchical Clustering: Customer Segmentation
Deprecated Guided Projects - 2020-04Neural Network from Scratch in TensorFlow
Deprecated Guided Projects - 2020-04Dimensionality Reduction using an Autoencoder in Python
Deprecated Guided Projects - 2020-04Anomaly Detection in Time Series Data with Keras
Deprecated Guided Projects - 2021-03Build your first Self Driving Car using AWS DeepRacer
Deprecated Guided Projects - 2021-06Creating Multi Task Models With Keras
Coursera - 2023-06Prompt Engineering for ChatGPT
Vanderbilt University - 2023-06Encoder-Decoder Architecture
Google Cloud - 2023-06Attention Mechanism→ F*********s
Google Cloud - 2023-06AI Art Generation for Everyone (DALL·E, Midjourney & Stable Diffusion)
OpenCV University - 2023-09Large Language Models
edX - 2023-11Trustworthy Generative AI
Vanderbilt University - 2023-11Generative AI Primer
Vanderbilt University - 2023-11Large Language Models: Application through Production
edX - 2023-11Fundamentals of Deep Learning for Multi-GPUs
NVIDIA Deep Learning Institute - 2023-12Advanced AI Art Generation
OpenCV University - 2024-05Large Language Models: Foundation Models from the Ground Up→ F*********s
edX - 2024-11Prompt Engineering Specialization
Vanderbilt University - 2024-11XCS224U – Natural Language Understanding
Stanford Online - 2024-11XCS236 – Deep Generative Models
Stanford Online - 2025-01Mastering OpenCV with Python
OpenCV University
Quantum22 credentials · 2015 to 2024surfaces at Bloomington Raleigh Indianapolis
Commentary to come. This thread is data first, essay second.
- 2015-05Exploring Quantum Physics
Coursera - 2018-028.370.1x: Quantum Information Science I, Part 1
edX - 2018-05Quantum 101: Quantum Computing & Quantum Internet
edX - 2018-05QTM1x: The Quantum Internet and Quantum Computers: How Will They Change the World?
edX - 2018-06QTM2x: The Building Blocks of a Quantum Computer: Part 1
edX - 2018-11QTM3x: The Building Blocks of a Quantum Computer: Part 2
edX - 2019-01QuCryptox: Quantum Cryptography
edX - 2019-04UTQML101x: Quantum Machine Learning→ F*********s
edX - 2020-08Quantum Mechanics and Quantum Computation
edX - 2020-11Quantum Computing For Everyone
edX - 2021-02Quantum Computing
edX - 2021-03Programming a Quantum Computer with Qiskit
Deprecated Guided Projects - 2021-03Quantum Computing. Less Formulas – More Understanding
Saint Petersburg State University - 2021-03Introduction to Quantum Computing for Everyone 2
edX - 2021-03Introduction to Quantum Computing – XSeries Completion
edX - 2021-06Getting Started with Quantum Machine Learning
Deprecated Guided Projects - 2021-06QTM4x: Fundamentals of Quantum Information
edX - 2023-11Quantum 301: Quantum Computing with Semiconductor Technology
edX - 2024-07Machine Learning for Semiconductor Quantum Devices
edX - 2024-07Development and Applications of Germanium Quantum Technologies
edX - 2024-11Density Functional Theory
École Polytechnique - 2024-11Quantum Computer Systems Design I: Intro to Quantum Computation and Programming
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Data and computation46 credentials · 2013 to 2021surfaces at OSG School, Madison SC24, Atlanta SC25, St. Louis
Commentary to come. This thread is data first, essay second.
- 2013-12An Introduction to Interactive Programming in Python
Coursera - 2014-05Computational Thinking using Python
edX - 2014-05Big Data and Social Physics
edX - 2014-05The Analytics Edge (with R)
edX - 2014-06Introduction to Systems Engineering
Coursera - 2014-06Sabermetrics 101: Introduction to Baseball Analytics (with R & SQL)
edX - 2014-08Introduction to Computer Science and Programming Using Python
edX - 2014-08Quantitative Biology Workshop (with Python & R)
edX - 2014-11Paradigms of Computer Programming – Fundamentals
edX - 2014-12Introduction to Computational Thinking and Data Science (with Python)
edX - 2015-02The Data Scientist’s Toolbox
Coursera - 2015-03Pattern Discovery in Data Mining
Coursera - 2015-03Discrete Time Signals and Systems, Part 1: Time Domain (with Matlab)
edX - 2015-05Getting and Cleaning Data
Coursera - 2015-05Process Mining: Data science in Action
Coursera - 2015-05R Programming
Coursera - 2015-06Introduction to Programming with MATLAB
Coursera - 2015-06Cluster Analysis in Data Mining
Coursera - 2015-07Principles of Computing (Part 1)
Coursera - 2015-07Introduction to Big Data with Apache Spark
edX - 2015-07Introduction to Cloud Computing
edX - 2015-07Systematic Program Design – Part 1
edX - 2015-08Data Visualization
Coursera - 2015-08Big Data XSeries
edX - 2015-09Big Data in Education
edX - 2015-10Python for Genomic Data Science
Coursera - 2015-10Exploratory Data Analysis
Coursera - 2015-11Developing Data Products
Coursera - 2016-01Introduction to Big Data (September 2015 Version)
Coursera - 2016-02Hadoop Platform and Application Framework
Coursera - 2016-03Biology Meets Programming: Bioinformatics for Beginners
Coursera - 2016-06Data Science Ethics
edX - 2016-11Using Databases with Python
Coursera - 2016-11Introduction to Big Data
Coursera - 2016-11Text Retrieval and Search Engines
Coursera - 2017-03Graph Analytics for Big Data
University of California San Diego - 2017-03Big Data Integration and Processing
University of California San Diego - 2017-05PH526x: Using Python for Research
edX - 2017-10Big Data Modeling and Management Systems
University of California San Diego - 2018-12DSE230x: Big Data Analytics using Spark
edX - 2019-03Security and Privacy for Big Data – Part 2
28DIGITAL - 2019-03Security and Privacy for Big Data – Part 1
28DIGITAL - 2019-09UT.PHP.16.01x: LAFF-On Programming for High Performance
edX - 2019-11NET04x: Advanced Algorithmics and Graph Theory with Python
edX - 2020-12Fundamentals of TinyML
edX - 2021-0769506x: Boltzmann Law: Physics to Computing
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Law, economics and society13 credentials · 2014 to 2025surfaces at Austin Asheville Open25, Las Vegas
Commentary to come. This thread is data first, essay second.
- 2014-04Gamification
Coursera - 2014-07An Introduction to Marketing
Coursera - 2014-12Social Network Analysis
Coursera - 2015-07Introduction to Operations Management
Coursera - 2015-12Internet Giants: The Law and Economics of Media Platforms
Coursera - 2016-11Introduction to Agent-Based Modeling
Santa Fe Institute / Complexity Explorer - 2021-02Fundamentals of Technical Analysis
edX - 2021-03Screening Stocks for Investment
Deprecated Guided Projects - 2023-11Narrative Economics
Yale University - 2024-07Group 2 Social / Behavioral Research Investigators and Key Personnel
CITI Program - 2024-07Responsible Conduct of Research for Students and Postdocs
CITI Program - 2024-12AI & Law
Lund University - 2025-01Human Excellence in the AI Age, Part 1: A Soft Skills Course
University of Virginia Darden School Foundation
Excursions4 credentials · 2015 to 2015surfaces at Savannah Huntsville
Commentary to come. This thread is data first, essay second.
- 2015-06Introduction to Genomic Technologies
Coursera - 2015-08Introduction to Thermodynamics: Transferring Energy from Here to There
Coursera - 2015-08Algorithms for DNA Sequencing
Coursera - 2015-10Proteins: Biology’s Workforce
edX
The lane below the coursework band is my reading, one dot a month since 2017, sized by books finished. It is an input like the others and it is not yet wired to any output, because 1,159 books cannot honestly be pointed at a particular result and the ones that can be have not been named yet. The Reading Room has the whole record, what I thought of each book, and the year the writing stopped.