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Understand the machine learning behind a prediction, then learn to build and run the system that makes it. This AI course is for programmers who want a rigorous, practical education, starting with the field's basic ideas and progressing toward production systems. Concepts, mathematics, code and engineering develop together: use a model on real data, examine why it works, and return to the idea at greater depth. Probability, statistics and Bayesian inference receive sustained attention alongside linear algebra, calculus and optimization. The curriculum moves through classical machine learning, neural networks and PyTorch, then opens up transformers and large language models. Later lessons cover retrieval-augmented generation, fine-tuning, AI agents and MLOps, including evaluation, deployment and monitoring on AWS and Google Cloud. Worked examples connect an intuition to a derivation, a piece of Python or a system design, giving you material to practice beyond the audio. AI news connects new research and product releases to the fundamentals that explain them. General programming fluency is assumed; prior machine learning study is not. This show's audio is narrated by an AI-generated synthetic voice.

Episodes

  1. 15h ago

    What Each Era of AI Tried, and Why It Stalled

    This chapter traces artificial intelligence from Turing's 1950 test through the Dartmouth meeting, two funding collapses, expert systems, statistical learning, and the deep learning and transformer breakthroughs, then applies the same evidentiary habits to a newly released AI model. Episode page & show notes Visit website A seventy-five-year argument about who supplies the rule The line drawn last time — between problems where a rule can be written down and problems where only examples can reveal it — turns out to be the entire history of artificial intelligence. Alan Turing opened it in 1950 by replacing "can a machine think" with a checkable game of blind interrogation, laid out in his original paper in Mind. Six years later the Dartmouth proposal bet that any feature of intelligence could be described precisely enough for a machine to simulate it, and the first decade delivered real wins in search, games, and Frank Rosenblatt's perceptron — a machine that adjusted its own weights from wrong answers. Then came the reckoning. Search exploded combinatorially on real problems, a single layer of weights proved unable to solve exclusive-or, and government reviews on both sides of the Atlantic — most bluntly the Lighthill Report — found promises with nothing measured behind them, and funding stopped. The next era hand-wrote expert knowledge instead, and it worked: XCON cut computer-configuration errors from over thirty percent to under two, saving Digital Equipment Corporation tens of millions a year, a story traced in the history of configuration technologies. It still collapsed under its own maintenance — half the rule base rewritten every year — because rules interact in ways nobody can see once the pile grows large enough. What replaced hand-written knowledge was counting from data: speech and statistical machine translation learned their structure from text rather than grammar books. Layered networks returned in 2012 when AlexNet beat hand-designed image features by eleven points on ImageNet, and in 2017 attention mechanisms let every position in a sequence look directly at every other, becoming the transformer behind today's large models. The chapter closes on what scaling has actually proven versus only claimed, and on five ideas — weight adjustment, held-out evaluation, learned probabilities, learned features, and attention — that survived every collapse along the way. Reading a release without the hype A current example puts that habit to work: Google's Gemini 3.6 Flash cuts output tokens by roughly seventeen percent and resolves about half of real software issues on one benchmark, yet an independent vision evaluation found its object-localization accuracy got worse — one release, improving on one measure while sliding on another.

  2. 1d ago

    Learning Rules From Examples Instead of Writing Them

    This chapter contrasts problems with hand-writable rules against problems like spam filtering and digit recognition where only examples work, sorts out five commonly confused terms in the field, and walks a vague "reduce churn" request into a fully specified, checkable prediction problem. Episode page & show notes Visit website When rules fail and examples work The chapter opens with two small programs that look equally simple but aren't. Withholding tax from a paycheck is a rule someone already wrote down — you code it in six lines and check it by hand. Spam filtering looks the same size but collapses: every fix (bad spelling, hidden HTML, image text, padding with harmless prose) is immediately worked around, and the rules start fighting each other until nobody can say what the filter does. Yet a person can still sort spam from real mail in seconds. That gap — recognizable but not describable, with plenty of examples on hand — is where learning from data belongs instead of hand-written logic, a distinction laid out clearly by pieces comparing rule-based and machine learning approaches to language tasks and when to choose one system over the other. Handwritten digit recognition tells the same story without any adversary involved: loops that don't close, sevens with and without a bar, forced the field toward systems like MNIST that learn the rule from labelled pixels instead of hand-coded shape logic. From there the chapter separates five words people use loosely and interchangeably — artificial intelligence, machine learning, deep learning, statistics, and data science — giving each one a problem it actually owns, from shortest-path search to A/B test confidence intervals to the messy plumbing of real datasets. It rejects the idea that these sit on a ladder with deep learning at the top, pointing out that a hand-written rule, a plain average, or a significance test is frequently the correct and more honest answer. Turning a wish into a stated problem The back half works a single example end to end: a vague request to "reduce churn" gets turned into a fully specified prediction problem — one subscriber, one month, features known before the fact (with a live demonstration of leakage via a cancellation-reason field filled in only after someone leaves), a yes/no target, historical labels drawn straight from billing records, and a baseline ("always say no," 96% accurate and useless) that any real model has to beat. It closes by turning to MNIST itself — sixty thousand labelled digit images reduced to raw pixel brightness with no notion of a line or curve — as the plainest way to see what a model is actually handed before it learns anything.

  • 2 Episodes

About

Understand the machine learning behind a prediction, then learn to build and run the system that makes it. This AI course is for programmers who want a rigorous, practical education, starting with the field's basic ideas and progressing toward production systems. Concepts, mathematics, code and engineering develop together: use a model on real data, examine why it works, and return to the idea at greater depth. Probability, statistics and Bayesian inference receive sustained attention alongside linear algebra, calculus and optimization. The curriculum moves through classical machine learning, neural networks and PyTorch, then opens up transformers and large language models. Later lessons cover retrieval-augmented generation, fine-tuning, AI agents and MLOps, including evaluation, deployment and monitoring on AWS and Google Cloud. Worked examples connect an intuition to a derivation, a piece of Python or a system design, giving you material to practice beyond the audio. AI news connects new research and product releases to the fundamentals that explain them. General programming fluency is assumed; prior machine learning study is not. This show's audio is narrated by an AI-generated synthetic voice.

Information

  • Creator
    OCDevel
  • Years Active
    2026
  • Episodes
    2
  • Rating
    Clean
  • Copyright
    © All rights reserved 2026, OCDevel
  • Show Website
    OCDevel AI