AI Bites: The Academic Series

Jack Lakkapragada
  • 0.0 (0)
  • COURSES
  • UPDATED DAILY

Welcome to AI Bites. This podcast features AI-generated deep dives into the world’s most prestigious computer science curricula. Based on personal study notes and publicly available course material from Stanford University (CS124, CS221, and more), these episodes use Google’s NotebookLM to transform dense academic topics into conversational summaries. Perfect for learning on the go, whether you're commuting or at the gym. Disclaimer: This is an independent, AI-generated study resource and is not officially affiliated with Stanford University.

Episodes

  1. 1D AGO

    CS124 in 7 minutes

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

    8 min

  2. 1D AGO

    CS221 in 7 minutes

    A quick video bite on CS221 and what the whole course is about - generated by Google NotebookLM from my class notes.

    7 min

  3. 3D AGO

    EP 09 | CS221 Week 6: Bayesian Learning, Logic & Final Wrap-up

    We’ve reached the finish line! 🏁 In our final installment of the Stanford CS221 series, we tackle the last pieces of the AI puzzle: moving from probabilistic reasoning to actual learning, and revisiting the power of symbolic logic. Key Topics: Deep Dive into Bayesian Learning: How we move beyond just using networks to actually learning their parameters and structures from data. Logic Problems: A return to the roots of AI. We explore propositional and first-order logic—how machines represent and reason through complex, rule-based knowledge. Course Conclusion: A bird’s eye view of everything we’ve covered, from Search and ML to Graphical Models and Logic. We connect the dots on what it truly means to build "Principles of AI." Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS221 materials and personal study notes.

    15 min

  4. 3D AGO

    EP 08 | CS221 Week 5: Markov Networks, Bayesian Networks & Inference

    We are moving from logic to probability! This week in Stanford’s CS221, we transition from the rigid rules of Constraint Satisfaction Problems (CSPs) into the flexible world of Probabilistic Graphical Models. We explore how AI handles uncertainty and models complex relationships between variables. Key Topics: Transition to Markov Networks: Moving beyond constraints to undirected graphical models that represent dependencies. Bayesian Networks: Understanding directed acyclic graphs (DAGs) and how they model causal relationships and conditional probabilities. Inference & Gibbs Sampling: How do we actually get answers from these complex networks? An introduction to sampling methods like Gibbs Sampling to estimate probabilities. Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS221 materials and personal study notes.

    17 min

  5. 3D AGO

    EP 07 | CS221 Week 4: Games & Constraint Satisfaction Problems

    How does AI play to win? This week in Stanford’s CS221, we shift gears from simple pathfinding to navigating adversarial environments and solving complex logic puzzles with strict boundaries. Key Topics: Games: Exploring strategic decision-making, game trees (Minimax/Expectimax), and how AI predicts an opponent’s next move in competitive scenarios. Introduction to CSPs: An entry into Constraint Satisfaction Problems. We look at how AI finds variables that satisfy a specific set of rules—essential for things like scheduling, map coloring, or Sudoku. Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS221 materials and personal study notes.

    15 min

  6. 3D AGO

    EP 06 | CS221 Week 3: Structured Perceptrons, A* Search & Markov Models

    We’re leveling up the complexity this week in Stanford’s CS221. We move from basic search into "smart" search and start exploring how AI handles sequences and uncertainty through Markov models. Key Topics:• Structured Perceptron: Moving beyond simple classification to predicting complex, structured outputs (like tags in a sentence or parts of an image).• A Search:* The "gold standard" of search algorithms. We look at how heuristics allow AI to find the most efficient path to a solution significantly faster.• Markov Problems: An introduction to modeling states and transitions. How do we make decisions when the future depends on the present? Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS221 materials and personal study notes.

    16 min

  7. 3D AGO

    EP 05 | CS221 Week 2: Optimization & The Search for Solutions

    We’re diving deeper into the engine room of Artificial Intelligence this week. We continue our journey through Stanford’s CS221, moving from basic ML foundations into the math of optimization and the logic of search algorithms. Key Topics:• Machine Learning 2 & 3: A deep dive into loss functions, gradient descent, and how we actually "train" a model to improve.• Search Problems 1 & 2: Understanding how AI navigates state spaces—from basic tree and graph searches to finding the most efficient paths to a goal. Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS221 materials and personal study notes.

    18 min

  8. 3D AGO

    EP 04 | CS221 Week 1: AI History, Ethics & ML Foundations

    New course alert! We are moving from NLP into Stanford’s flagship AI course: CS221 (Principles of AI). We kick things off by looking at the "big picture"—where AI came from, the ethics of what we’re building, and the start of our Machine Learning journey. Key Topics:• Course Introduction: Defining the "Principles" of Artificial Intelligence.• AI History: Tracing the path from early logic to modern neural networks.• AI Ethics: The critical considerations of bias, safety, and societal impact.• Machine Learning 1: Starting the deep dive into the core mechanics of ML. Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS221 materials and personal study notes.

    20 min

  9. 5D AGO

    EP 03 | CS124 Week 3: Speech, Recommendations & Networks

    In our third installment, we look at how NLP and AI interact with the broader world, from human speech recognition to the algorithms that decide what shows up in your social feed. Key Topics:• Speech Recognition: Converting audio to data.• Recommendation Systems: The AI behind "You might also like..."• Social Networks & Web Links: How PageRank organizes the internet. Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS124 materials and personal study notes.

    16 min

  10. 5D AGO

    EP 02 | CS124 Week 2: Embeddings, LLMs & Transformers

    Things get "deep" this week. We trace the evolution of how AI understands the meaning of words—moving from simple vectors to the revolutionary Transformer architecture that powers today’s LLMs. Key Topics:• Vector & Contextual Embeddings: Mathematical meaning.• Neural Networks: Biological inspiration.• LLMs & Transformers: The breakthrough that changed everything. Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS124 materials and personal study notes.

    16 min

  11. 5D AGO

    EP 01 | CS124 Week 1: Words, N-grams & Information Retrieval

    We kick off our deep dive into Stanford’s CS124 with the building blocks of NLP. This episode covers how computers "read" and organize language before moving into the math behind search. Key Topics:• Words & Tokens: How we break down language.• N-gram Language Modeling: Predicting sequences.• Logistic Regression: The classification workhorse.• Information Retrieval: How search engines work. Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS124 materials and personal study notes.

    18 min
  • 11 Episodes

About

Welcome to AI Bites. This podcast features AI-generated deep dives into the world’s most prestigious computer science curricula. Based on personal study notes and publicly available course material from Stanford University (CS124, CS221, and more), these episodes use Google’s NotebookLM to transform dense academic topics into conversational summaries. Perfect for learning on the go, whether you're commuting or at the gym. Disclaimer: This is an independent, AI-generated study resource and is not officially affiliated with Stanford University.

Information