A model can have 99% accuracy and still fail your users. In this episode, we tackle Model Evaluation from the Product Manager's perspective. We bridge the gap between technical model metrics (what engineers care about) and product/business metrics (what stakeholders care about).

Key Topics:

• The Accuracy Trap: Why "Accuracy" is often a misleading metric, especially with imbalanced datasets.

• The Confusion Matrix: Breaking down True/False Positives and Negatives so you can visualize exactly where your model is making mistakes.

• Precision vs. Recall: The ultimate PM trade-off. Should you optimize to catch every single edge case (high recall) or ensure every alert is perfectly correct (high precision)?

• System vs. Business Metrics: Balancing model performance with latency, user task success rates, and ultimate ROI.

Note: This is an AI-generated study resource created via NotebookLM based on Duke University’s ML for Product Managers curriculum and personal study notes.

Building a model is about more than just data—it’s about a repeatable process. This episode walks through the lifecycle of a machine learning project, focusing on the strategic decisions a Product Manager must navigate to ensure a model is production-ready.

Key Topics:

• The 5-Step Process: From problem definition and data collection to model evaluation.

• Feature & Algorithm Selection: How PMs influence which data is used and which model "flavor" fits the business goal.

• The Bias-Variance Tradeoff: Understanding model complexity so you can troubleshoot "underfitting" or "overfitting" with your engineering team.

• Validation & Testing: Why we use separate sets to prove a model actually works before it hits the real world.

• Cross-Validation: Ensuring your model’s performance isn't just a fluke of the data.

Note: This is an AI-generated study resource created via NotebookLM based on Duke University’s ML for Product Managers curriculum and personal study notes.

We kick off a new course from Duke University designed specifically for those leading AI products. In this episode, we strip away the code and focus on the core vocabulary and intuition every Product Manager needs to collaborate effectively with data scientists.

Key Topics:

• Defining ML for Business: What machine learning actually is and—more importantly—what it is not.

• The PM’s Vocabulary: Breaking down "Data Terminology," from features and labels to training sets.

• The 3 Types of ML: A high-level look at Supervised, Unsupervised, and Reinforcement Learning.

• Possibility vs. Reality: A critical discussion on what ML can do well and where it typically fails (or shouldn't be used at all).

Note: This is an AI-generated study resource created via NotebookLM based on Duke University’s ML for Product Managers curriculum and personal study notes.

Short on time? We’ve distilled the entire Stanford CS21SI: AI for Social Good course into a single 10-minute video. This "Cram Session" covers the journey from ethical frameworks to technical execution across four major domains of social impact.

Watch to see the "Best Of" our course deep dives:

• Education: The Minerva High School case study and the pitfalls of "Magic Box" metrics.

• Environment: Deep Learning vs. the Wildfire crisis and the carbon cost of AI.

• Information: NLP, Transformers, and the battle against automated disinformation.

• Accessibility & Conservation: Computer Vision for healthcare and Reinforcement Learning for wildlife protection.

This video is the ultimate summary of our "For AI, By AI, To AI" experiment, synthesized via NotebookLM and Gemini from Stanford's open-source curriculum.

Note: This is an AI-generated study resource.

Description:We’ve reached the finish line for Stanford’s CS21SI! In our final episode, we move beyond passive observation and into the world of sequential decision-making. We explore how Reinforcement Learning (RL) allows AI to learn through trial and error—and why that’s a game-changer for protecting our planet's most endangered species.

Key Topics:

• From Perception to Action: Why "Social Good" isn't a one-time classification, but a series of high-stakes decisions.

• The PAWS Case Study: How park rangers use RL to outsmart poachers in a high-tech game of cat-and-mouse.

• Exploration vs. Exploitation: The "core heartbeat" of RL and the human dilemma of trying new solutions in risky environments.

• The Math of Value: A high-level look at Markov Decision Processes (MDPs) and the Bellman Equation (The "Wisdom of the Future").

• Ethical Guardrails: The dangers of "Reward Hacking" and why we must involve the community (Participatory Design) to define what a "good outcome" actually looks like.

Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS21SI materials and personal study notes.

We’re exploring how machines "see" and how that vision can be used to restore human agency. In this installment of Stanford’s CS21SI, we move from the technical mechanics of Convolutional Neural Networks (CNNs) to life-changing applications in healthcare, accessibility, and conservation.

Key Topics:

• The Social Model of Disability: Reframing disability not as a medical failure, but as a limit to agency that inclusive technology can solve.

• CNNs Under the Hood: Understanding filters, convolution, and why biological inspiration from the visual cortex is the secret to processing pixels.

• Case Study: "Autism Glass": A look at wearable tech designed to assist with affect recognition in real-time.

• Global Impact: From identifying skin cancer and tracking biodiversity to accelerating disaster relief via satellite imagery.

• The Human-in-the-Loop: Why interpretability and "Saliency Maps" are critical when AI assists in high-stakes medical decisions.

Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS21SI materials and personal study notes.

Description:How does AI decide what is "true"? This week, we dive into Natural Language Processing (NLP). We trace the evolution from RNNs to Transformers and discuss the massive threat Large Language Models (LLMs) pose to our information ecosystems and democracy.

Key Topics:

• The Disinformation Threat: How GPT-class models can generate coherent, grammatical, and completely fictional "news."

• RNNs vs. Transformers: Why "Attention is All You Need"—moving from linear memory to simultaneous context processing.

• Case Study: Fake News: Understanding how AI-generated text is built so we can better learn how to defend against it.

• Word Embeddings: How we turn the "messiness" of human language into numbers that machines can actually process.

Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS21SI materials and personal study notes.

We’re moving from basic ML into the technical engine of modern AI: Deep Learning. In this week of Stanford’s CS21SI, we explore how neural networks can model complex, non-linear realities—specifically applying them to the escalating global wildfire crisis.

Key Topics:

• The Neural Analogy: Understanding neurons, forward/backward passes, and backpropagation through the lens of social impact.

• Wildfire Case Study: Using DL for spread prediction, forecasting, and the ethical dilemma of optimizing resource allocation for first responders.

• The "Hidden Labor": A critical look at the human cost of AI—from data labeling "sweatshops" to the exploitation of incarcerated firefighters.

• Environmental Footprint: Discussing the water and carbon cost of training massive deep learning models.

Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS21SI materials and personal study notes.

We’re kicking off a brand new series! This week we dive into CS21SI, a course that bridges the gap between technical AI development and its real-world impact. We start at the beginning: how machines learn and the ethical guardrails we need to build alongside them.

Key Topics:

• Intro to Machine Learning: A high-level look at how models learn from data patterns.

• AI Ethics: Moving beyond the code to discuss bias, fairness, and the responsibility of the AI creator.

• Impactful AI: Understanding the societal implications of the models we deploy.

Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS21SI materials and personal study notes.

A quick and easy way to understand CS124 in a quick video byte. Generated by Google NotebookLM using my personal class notes.