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OCDevel

Machine learning audio course, teaching the fundamentals of machine learning and artificial intelligence. It covers intuition, models (shallow and deep), math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.

Machine Learning Guide

Links:
 
 • Notes and resources at 
 ocdevel.com/mlg/33 • 3Blue1Brown videos: 
https://3blue1brown.com/ Try a walking desk • stay healthy & sharp while you learn & code
 Try Descript • audio/video editing with AI power-tools
 Background & Motivation RNN Limitations: • Sequential processing prevents full parallelization—even with attention tweaks—making them inefficient on modern hardware.
 Breakthrough: • “Attention Is All You Need” replaced recurrence with self-attention, unlocking massive parallelism and scalability.
 Core Architecture Layer Stack: • Consists of alternating self-attention and feed-forward (MLP) layers, each wrapped in residual connections and layer normalization.
 Positional Encodings: • Since self-attention is permutation invariant, add sinusoidal or learned positional embeddings to inject sequence order.
 Self-Attention Mechanism Q, K, V Explained: • 
 Query (Q): • The representation of the token seeking contextual info.
 Key (K): • The representation of tokens being compared against.
 Value (V): • The information to be aggregated based on the attention scores.
 • 
 Multi-Head Attention: • Splits Q, K, V into multiple “heads” to capture diverse relationships and nuances across different subspaces.
 Dot-Product & Scaling: • Computes similarity between Q and K (scaled to avoid large gradients), then applies softmax to weigh V accordingly.
 Masking Causal Masking: • In autoregressive models, prevents a token from “seeing” future tokens, ensuring proper generation.
 Padding Masks: • Ignore padded (non-informative) parts of sequences to maintain meaningful attention distributions.
 Feed-Forward Networks (MLPs) Transformation & Storage: • Post-attention MLPs apply non-linear transformations; many argue they’re where the “facts” or learned knowledge really get stored.
 Depth & Expressivity: • Their layered nature deepens the model’s capacity to represent complex patterns.
 Residual Connections & Normalization Residual Links: • Crucial for gradient flow in deep architectures, preventing vanishing/exploding gradients.
 Layer Normalization: • Stabilizes training by normalizing across features, enhancing convergence.
 Scalability & Efficiency Considerations Parallelization Advantage: • Entire architecture is designed to exploit modern parallel hardware, a huge win over RNNs.
 Complexity Trade-offs: • Self-attention’s quadratic complexity with sequence length remains a challenge; spurred innovations like sparse or linearized attention.
 Training Paradigms & Emergent Properties Pretraining & Fine-Tuning: • Massive self-supervised pretraining on diverse data, followed by task-specific fine-tuning, is the norm.
 Emergent Behavior: • With scale comes abilities like in-context learning and few-shot adaptation, aspects that are still being unpacked.
 Interpretability & Knowledge Distribution Distributed Representation: • “Facts” aren’t stored in a single layer but are embedded throughout both attention heads and MLP layers.
 Debate on Attention: • While some see attention weights as interpretable, a growing view is that real “knowledge” is diffused across the network’s parameters.

MLG 033 Transformers

Links
 
 • Notes and resources at 
 ocdevel.com/mlg/mla-22 Try a walking desk • stay healthy & sharp while you learn & code
 Try Descript • audio/video editing with AI power-tools
 
I currently favor Roo Code. Plus either gemini-2.5-pro-exp-03-25 for Architect, Boomerang, or Code with large contexts. And Claude 3.7 for code with small contexts, eg Boomerang subtasks. Many others favor Cursor, Aider, or Cline. Copilot and Windsurf are less vogue lately. I found Copilot to struggle more; and their pricing - previously their winning point - is less compelling now.
 
Why I favor Roo. The default settings have it as stable and effective as Cline, Cursor. But you can tinker more with these settings - eg, for Gemini 2.5 I disable partial file reads (since it has a huge context window). Their modes are elegantly just custom system prompts (an oversimplification), making custom workflows very powerful. A potent example is their Boomerang Mode, which is an orchestrator that delegates planning and edit subtasks, to keep context windows tight. Boomerang mode specifically is a plugin-seller, it's incredibly powerful. Aider is still a darn decent exacto-knife, but as Roo has grown, I haven't found much need for Aider.
 
Tools discussed:
 
 Roo Code Aider Cursor Cline Copilot Windsurf 
Other:
 
 Leaderboards Video of speed-demon Reddit 
"Vibe coding" using AI agents in software development. It uses LLMs for code generation and project management. Developers are increasingly relying on agentic tools and IDE plugins to improve productivity.
 Use of AI in Code Generation • AI tools facilitate the generation and editing of code.
 • Integration typically occurs within IDEs or as plugins.
 • These tools offer features like inline editing, bug fixing, and project scaffolding.
 Evolution and Adoption • The concept is gaining popularity due to its efficiency and competitive edge in development.
 Popular AI Tools for Vibe Coding Cursor Characteristics • : Most popular, stable, with advanced agentic capabilities.
 Pricing • : $20 per month, additional charges for power-use.
 Strengths • : Reliable, focuses on integrating new models effectively.
 Windsurf Characteristics • : Cost-effective, a VS Code fork.
 Pricing • : Starts at $15, with higher usage at $60.
 Strengths • : Similar to Cursor, with a competitive pricing model.
 GitHub Copilot Characteristics • : Operates within GitHub code spaces, developed by Microsoft.
 Pricing • : $10 to $40 monthly.
 Strengths • : Deep integration with cloud-based development environments.
 Cline Characteristics • : Open-source, known for customizable features.
 Pricing • : BYOM (Bring Your Own Model), costs based on individual API usage.
 Strengths • : Community-driven, rapid development cycles.
 Roo Code Characteristics • : Fast-moving, offers the latest technological advancements.
 Pricing • : Uses BYOM model, similar to Cline.
 Strengths • : Frequent updates, for users wanting cutting-edge features.
 Aider Characteristics • : CLI-based, focuses on precision and minimal token usage.
 Pricing • : BYOM, efficient token usage strategies.
 Strengths • : High accuracy for small adjustments, good for backup use.
 Choosing the Right Tool Beginner Recommendation • : Start with Cursor for reliability.
 Experimentation • : Try Copilot and Windsurf for comparisons.
 Advanced Configuration • : Use Kline or Roo Code for sophisticated tasks and ER for precise adjustments.
 Cost Management Open Router • : Centralize API billing to manage interactions across multiple models, preventing fragmented payments.

MLA 022 Code AI Tools

Links
 
 • Notes and resources at 
 ocdevel.com/mlg/mla-23 Try a walking desk • stay healthy & sharp while you learn & code
 Try Descript • audio/video editing with AI power-tools
 Model Current Leaders 
According to the Aider Leaderboard (as of April 12, 2025), leading models include for vibe-coding:
 
 Gemini 2.5 Pro Preview 03-25 • : most accurate and cost-effective option currently.
 Claude 3.7 Sonnet • : Performs well in both architect and code modes with enabled reasoning flags.
 DeepSeek R1 with Claude 3.5 Sonnet • : A popular combination for its balance of cost and performance between reasoning and non-reasoning tasks.
 Local Models Tools for Local Models • : 
Ollama • is the standard tool to manage local models, enabling usage without internet connectivity.
 Privacy and Security • : Utilizing local models enhances data security, suitable for sensitive projects or corporate environments that require data to remain onsite.
 Performance Trade-offs • : Local models, due to distillation and size constraints, often perform slightly worse than cloud-hosted models but offer privacy benefits.
 Fine-Tuning Models Customization • : Developers can fine-tune pre-trained models to specialize them for their specific codebase, enhancing relevance and accuracy.
 Advanced Usage • : Suitable for long-term projects, fine-tuning helps models understand unique aspects of a project, resulting in consistent code quality improvements.
 Tips and Best Practices Judicious Use of the @ Key • : Improves model efficiency by specifying the context of commands, reducing the necessity for AI-initiated searches. 
 • Examples include specifying file paths, URLs, or git commits to inform AI actions more precisely.
 • 
 Concurrent Feature Implementation • : Leverage tools like 
Boomerang mode • to manage multiple features simultaneously, acting more as a manager overseeing several tasks at once, enhancing productivity.
 Continued Learning • : Staying updated with documentation, particularly 
Roo Code • 's, due to its comprehensive feature set and versatility among AI coding tools.

MLA 023 Code AI Models & Modes

Model Context Protocol (MCP) standardizes tool communication, enabling AI coding agents to perform complex tasks like executing commands, interacting with web browsers, and integrating local or cloud resources. MCP servers broaden AI applications beyond coding. In machine learning, use AI tools to help optimizing data engineering, model deployment, and augmenting typical machine learning tasks.
 Links • Notes and resources at 
 ocdevel.com/mlg/mla-24 Try a walking desk • stay healthy & sharp while you learn & code
 Try Descript • audio/video editing with AI power-tools
 Tool Use in AI Code Agents File Operations • : Agents can read, edit, and search files using sophisticated regular expressions.
 Executable Commands • : They can recommend and perform installations like 
pip • or 
npm • installs, with user approval.
 Browser Integration • : Allows agents to perform actions and verify outcomes through browser interactions.
 Model Context Protocol (MCP) Standardization • : MCP was created by Anthropic to standardize how AI tools and agents communicate with each other and with external tools.
 Implementation • : 
 MCP Client • : Converts AI agent requests into structured commands.
 MCP Server • : Executes commands and sends structured responses back to the client.
 • 
 Local and Cloud Frameworks • : 
 Local (S-T-D-I-O MCP) • : Examples include utilizing Playwright for local browser automation and connecting to local databases like Postgres.
 Cloud (SSE MCP) • : SaaS providers offer cloud-hosted MCPs to enhance external integrations.
 • 
 Expanding AI Capabilities with MCP Servers Directories • : Various directories exist listing MCP servers for diverse functions beyond programming. 
modelcontextprotocol/servers Use Cases • : 
 Automation Beyond Coding • : Implementing MCPs that extend automation into non-programming tasks like sales, marketing, or personal project management.
 Creative Solutions • : Encourages innovation in automating routine tasks by integrating diverse MCP functionalities.
 • 
 AI Tools in Machine Learning Automating ML Process • : 
 Auto ML and Feature Engineering • : AI tools assist in transforming raw data, optimizing hyperparameters, and inventing new ML solutions.
 Pipeline Construction and Deployment • : Facilitates the use of infrastructure as code for deploying ML models efficiently.
 • 
 Active Experimentation • : 
 Jupyter Integration Challenges • : While integrations are possible, they often lag and may not support the latest models.
 Practical Strategies • : Suggests alternating between Jupyter and traditional Python files to maximize tool efficiency.
 • 
 Action Plan for ML Engineers • : 
 • Setup structured folders and documentation to leverage AI tools effectively.
 • Encourage systematic exploration of MCPs to enhance both direct programming tasks and associated workflows.
 •

MLA 024 Code AI MCP Servers, ML Engineering

Explains language models (LLMs) advancements. Scaling laws - the relationships among model size, data size, and compute - and how emergent abilities such as in-context learning, multi-step reasoning, and instruction following arise once certain scaling thresholds are crossed. The evolution of the transformer architecture with Mixture of Experts (MoE), describes the three-phase training process culminating in Reinforcement Learning from Human Feedback (RLHF) for model alignment, and explores advanced reasoning techniques such as chain-of-thought prompting which significantly improve complex task performance.
 Links • Notes and resources at 
ocdevel.com/mlg/mlg34 • Build the future of multi-agent software with 
 AGNTCY Try a walking desk • stay healthy & sharp while you learn & code
 Transformer Foundations and Scaling Laws Transformers • : Introduced by the 2017 "Attention is All You Need" paper, transformers allow for parallel training and inference of sequences using self-attention, in contrast to the sequential nature of RNNs.
 Scaling Laws • : 
 • Empirical research revealed that LLM performance improves predictably as model size (parameters), data size (training tokens), and compute are increased together, with diminishing returns if only one variable is scaled disproportionately.
 • The "Chinchilla scaling law" (DeepMind, 2022) established the optimal model/data/compute ratio for efficient model performance: earlier large models like GPT-3 were undertrained relative to their size, whereas right-sized models with more training data (e.g., Chinchilla, LLaMA series) proved more compute and inference efficient.
 • 
 Emergent Abilities in LLMs Emergence • : When trained beyond a certain scale, LLMs display abilities not present in smaller models, including: 
 In-Context Learning (ICL) • : Performing new tasks based solely on prompt examples at inference time.
 Instruction Following • : Executing natural language tasks not seen during training.
 Multi-Step Reasoning & Chain of Thought (CoT) • : Solving arithmetic, logic, or symbolic reasoning by generating intermediate reasoning steps.
 • 
 Discontinuity & Debate • : These abilities appear abruptly in larger models, though recent research suggests that this could result from non-linearities in evaluation metrics rather than innate model properties.
 Architectural Evolutions: Mixture of Experts (MoE) MoE Layers • : Modern LLMs often replace standard feed-forward layers with MoE structures. 
 • Composed of many independent "expert" networks specializing in different subdomains or latent structures.
 • A gating network routes tokens to the most relevant experts per input, activating only a subset of parameters—this is called "sparse activation."
 • Enables much larger overall models without proportional increases in compute per inference, but requires the entire model in memory and introduces new challenges like load balancing and communication overhead.
 • 
 Specialization & Efficiency • : Experts learn different data/knowledge types, boosting model specialization and throughput, though care is needed to avoid overfitting and underutilization of specialists.
 The Three-Phase Training Process 1. Unsupervised Pre-Training • : Next-token prediction on massive datasets—builds a foundation model capturing general language patterns.
 2. Supervised Fine Tuning (SFT) • : Training on labeled prompt-response pairs to teach the model how to perform specific tasks (e.g., question answering, summarization, code generation). Overfitting and "catastrophic forgetting" are risks if not carefully managed.
 3. Reinforcement Learning from Human Feedback (RLHF) • : 
 • Collects human preference data by generating multiple responses to prompts and then having annotators rank them.
 • Builds a reward model (often PPO) based on these rankings, then updates the LLM to maximize alignment with human preferences (helpfulness, harmlessness, truthfulness).
 • Introduces complexity and risk of reward hacking (specification gaming), where the model may exploit the reward system in unanticipated ways.
 • 
 Advanced Reasoning Techniques Prompt Engineering • : The art/science of crafting prompts that elicit better model responses, shown to dramatically affect model output quality.
 Chain of Thought (CoT) Prompting • : Guides models to elaborate step-by-step reasoning before arriving at final answers—demonstrably improves results on complex tasks. 
 • Variants include zero-shot CoT ("let's think step by step"), few-shot CoT with worked examples, self-consistency (voting among multiple reasoning chains), and Tree of Thought (explores multiple reasoning branches in parallel).
 • 
 Automated Reasoning Optimization • : Frontier models selectively apply these advanced reasoning techniques, balancing compute costs with gains in accuracy and transparency.
 Optimization for Training and Inference Tradeoffs • : The optimal balance between model size, data, and compute is determined not only for pretraining but also for inference efficiency, as lifetime inference costs may exceed initial training costs.
 Current Trends • : Efficient scaling, model specialization (MoE), careful fine-tuning, RLHF alignment, and automated reasoning techniques define state-of-the-art LLM development.

MLG 034 Large Language Models 1

At inference, large language models use in-context learning with zero-, one-, or few-shot examples to perform new tasks without weight updates, and can be grounded with Retrieval Augmented Generation (RAG) by embedding documents into vector databases for real-time factual lookup using cosine similarity. LLM agents autonomously plan, act, and use external tools via orchestrated loops with persistent memory, while recent benchmarks like GPQA (STEM reasoning), SWE Bench (agentic coding), and MMMU (multimodal college-level tasks) test performance alongside prompt engineering techniques such as chain-of-thought reasoning, structured few-shot prompts, positive instruction framing, and iterative self-correction.
 Links • Notes and resources at 
ocdevel.com/mlg/mlg35 • Build the future of multi-agent software with 
 AGNTCY Try a walking desk • stay healthy & sharp while you learn & code
 In-Context Learning (ICL) Definition: • LLMs can perform tasks by learning from examples provided directly in the prompt without updating their parameters. 
 Types: • 
 Zero-shot • : Direct query, no examples provided.
 One-shot • : Single example provided.
 Few-shot • : Multiple examples, balancing quantity with context window limitations.
 • 
 Mechanism: • ICL works through analogy and Bayesian inference, using examples as semantic priors to activate relevant internal representations.
 Emergent Properties: • ICL is an "inference-time training" approach, leveraging the model’s pre-trained knowledge without gradient updates; its effectiveness can be enhanced with diverse, non-redundant examples.
 • 
 Retrieval Augmented Generation (RAG) and Grounding Grounding: • Connecting LLMs with external knowledge bases to supplement or update static training data. 
 Motivation • : LLMs’ training data becomes outdated or lacks proprietary/specialized knowledge.
 Benefit • : Reduces hallucinations and improves factual accuracy by incorporating current or domain-specific information.
 • 
 RAG Workflow: • 
 Embedding: 1. Documents are converted into vector embeddings (using sentence transformers or representation models).
 Storage: 2. Vectors are stored in a vector database (e.g., FAISS, ChromaDB, Qdrant).
 Retrieval: 3. When a query is made, relevant chunks are extracted based on similarity, possibly with re-ranking or additional query processing.
 Augmentation: 4. Retrieved chunks are added to the prompt to provide up-to-date context for generation.
 Generation: 5. The LLM generates responses informed by the augmented context.
 • 
 Advanced RAG: • Includes agentic approaches—self-correction, aggregation, or multi-agent contribution to source ingestion, and can integrate external document sources (e.g., web search for real-time info, or custom datasets for private knowledge).
 • 
 LLM Agents Overview: • Agents extend LLMs by providing goal-oriented, iterative problem-solving through interaction, memory, planning, and tool usage.
 Key Components: • 
 Reasoning Engine (LLM Core): • Interprets goals, states, and makes decisions.
 Planning Module: • Breaks down complex tasks using strategies such as Chain of Thought or ReAct; can incorporate reflection and adjustment.
 Memory: • Short-term via context window; long-term via persistent storage like RAG-integrated databases or special memory systems.
 Tools and APIs: • Agents select and use external functions—file manipulation, browser control, code execution, database queries, or invoking smaller/fine-tuned models.
 • 
 Capabilities: • Support self-evaluation, correction, and multi-step planning; allow integration with other agents (multi-agent systems); face limitations in memory continuity, adaptivity, and controllability.
 Current Trends: • Research and development are shifting toward these agentic paradigms as LLM core scaling saturates.
 Multimodal Large Language Models (MLLMs) Definition: • Models capable of ingesting and generating across different modalities (text, image, audio, video).
 Architecture: • 
 Modality-Specific Encoders: • Convert raw modalities (text, image, audio) into numeric embeddings (e.g., vision transformers for images).
 Fusion/Alignment Layer: • Embeddings from different modalities are projected into a shared space, often via cross-attention or concatenation, allowing the model to jointly reason about their content.
 Unified Transformer Backbone: • Processes fused embeddings to allow cross-modal reasoning and generates outputs in the required format.
 • 
 Recent Advances: • Unified architectures (e.g., GPT-4o) use a single model for all modalities rather than switching between separate sub-models.
 Functionality: • Enables actions such as image analysis via text prompts, visual Q&A, and integrated speech recognition/generation.
 Advanced LLM Architectures and Training Directions Predictive Abstract Representation: • Incorporating latent concept prediction alongside token prediction (e.g., via autoencoders).
 Patch-Level Training: • Predicting larger “patches” of tokens to reduce sequence lengths and computation.
 Concept-Centric Modeling: • Moving from next-token prediction to predicting sequences of semantic concepts (e.g., Meta’s Large Concept Model).
 Multi-Token Prediction: • Training models to predict multiple future tokens for broader context capture.
 Evaluation Benchmarks (as of 2025) Key Benchmarks Used for LLM Evaluation: • 
 GPQA (Diamond): • Graduate-level STEM reasoning.
 SWE Bench Verified: • Real-world software engineering, verifying agentic code abilities.
 MMMU: • Multimodal, college-level cross-disciplinary reasoning.
 HumanEval: • Python coding correctness.
 HLE (Human’s Last Exam): • Extremely challenging, multimodal knowledge assessment.
 LiveCodeBench: • Coding with contamination-free, up-to-date problems.
 MLPerf Inference v5.0 Long Context: • Throughput/latency for processing long contexts.
 MultiChallenge Conversational AI: • Multiturn dialogue, in-context reasoning.
 TAUBench/PFCL: • Tool utilization in agentic tasks.
 TruthfulnessQA: • Measures tendency toward factual accuracy/robustness against misinformation.
 • 
 Prompt Engineering: High-Impact Techniques Foundational Approaches: • 
 Few-Shot Prompting: • Provide pairs of inputs and desired outputs to steer the LLM.
 Chain of Thought: • Instructing the LLM to think step-by-step, either explicitly or through internal self-reprompting, enhances reasoning and output quality.
 Clarity and Structure: • Use clear, detailed, and structured instructions—task definition, context, constraints, output format, use of delimiters or markdown structuring.
 Affirmative Directives: • Phrase instructions positively (“write a concise summary” instead of “don’t write a long summary”).
 Iterative Self-Refinement: • Prompt the LLM to review and improve its prior response for better completeness, clarity, and factuality.
 System Prompt/Role Assignment: • Assign a persona or role to the LLM for tailored behavior (e.g., “You are an expert Python programmer”).
 • 
 Guideline: • Regularly consult official prompting guides from model developers as model capabilities evolve.
 Trends and Research Outlook Inference-time compute • is increasingly important for pushing the boundaries of LLM task performance.
 Agentic LLMs • and 
multimodal reasoning • represent the primary frontiers for innovation.
 Prompt engineering • and 
benchmarking • remain essential for extracting optimal performance and assessing progress.
 • Models are expected to continue evolving with research into new architectures, memory systems, and integration techniques.

MLG 035 Large Language Models 2

Show notes: ocdevel.com/mlg/1. MLG teaches the fundamentals of machine learning and artificial intelligence. It covers intuition, models, math, languages, frameworks, etc. Where your other ML resources provide the trees, I provide the forest. Consider MLG your syllabus, with highly-curated resources for each episode's details at ocdevel.com. Audio is a great supplement during exercise, commute, chores, etc.
 MLG • , 
Resources Guide • Gnothi (podcast project): 
website • , 
Github What is this podcast? • "Middle" level overview (deeper than a bird's eye view of machine learning; higher than math equations)
 • No math/programming experience required
 
Who is it for
 
 • Anyone curious about machine learning fundamentals
 • Aspiring machine learning developers
 
Why audio?
 
 • Supplementary content for commute/exercise/chores will help solidify your book/course-work
 
What it's not
 
 • News and Interviews: TWiML and AI, O'Reilly Data Show, Talking machines
 • Misc Topics: Linear Digressions, Data Skeptic, Learning machines 101
 • iTunesU issues
 
Planned episodes
 
 • What is AI/ML: definition, comparison, history
 • Inspiration: automation, singularity, consciousness
 • ML Intuition: learning basics (infer/error/train); supervised/unsupervised/reinforcement; applications
 • Math overview: linear algebra, statistics, calculus
 • Linear models: supervised (regression, classification); unsupervised
 • Parts: regularization, performance evaluation, dimensionality reduction, etc
 • Deep models: neural networks, recurrent neural networks (RNNs), convolutional neural networks (convnets/CNNs)
 • Languages and Frameworks: Python vs R vs Java vs C/C++ vs MATLAB, etc; TensorFlow vs Torch vs Theano vs Spark, etc

MLG 001 Introduction

Links:
 
 • Notes and resources at 
 ocdevel.com/mlg/2 Try a walking desk • stay healthy & sharp while you learn & code
 Try Descript • audio/video editing with AI power-tools
 
What is artificial intelligence, machine learning, and data science? What are their differences? AI history.
 
Hierarchical breakdown: DS(AI(ML)). Data science: any profession dealing with data (including AI & ML). Artificial intelligence is simulated intellectual tasks. Machine Learning is algorithms trained on data to learn patterns to make predictions.
 Artificial Intelligence (AI) - Wikipedia 
Oxford Languages: the theory and development of computer systems able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
 
AlphaGo Movie, very good!
 
Sub-disciplines
 
 • Reasoning, problem solving
 • Knowledge representation
 • Planning
 • Learning
 • Natural language processing
 • Perception
 • Motion and manipulation
 • Social intelligence
 • General intelligence
 
Applications
 
 • Autonomous vehicles (drones, self-driving cars)
 • Medical diagnosis
 • Creating art (such as poetry)
 • Proving mathematical theorems
 • Playing games (such as Chess or Go)
 • Search engines
 • Online assistants (such as Siri)
 • Image recognition in photographs
 • Spam filtering
 • Prediction of judicial decisions
 • Targeting online advertisements
 Machine Learning (ML) - Wikipedia 
Oxford Languages: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyze and draw inferences from patterns in data.
 Data Science (DS) - Wikipedia 
Wikipedia: Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from noisy, structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.
 History • Greek mythology, Golums
 • First attempt: Ramon Lull, 13th century
 • Davinci's walking animals
 • Descartes, Leibniz
 • 

1700s-1800s: Statistics & Mathematical decision making
 • 

 • Thomas Bayes: reasoning about the probability of events
 • George Boole: logical reasoning / binary algebra
 • Gottlob Frege: Propositional logic
 • 
 • 1832: Charles Babbage & Ada Byron / Lovelace: designed Analytical Engine (1832), programmable mechanical calculating machines
 • 

1936: Universal Turing Machine
 • 

 • Computing Machinery and Intelligence - explored AI!
 • 
 • 1946: John von Neumann Universal Computing Machine
 • 1943: Warren McCulloch & Walter Pitts: cogsci rep of neuron; Frank Rosemblatt uses to create Perceptron (-> neural networks by way of MLP)
 • 

50s-70s: "AI" coined @Dartmouth workshop 1956 - goal to simulate all aspects of intelligence. John McCarthy, Marvin Minksy, Arthur Samuel, Oliver Selfridge, Ray Solomonoff, Allen Newell, Herbert Simon
 • 

 • Newell & Simon: Hueristics -> Logic Theories, General Problem Solver
 • Slefridge: Computer Vision
 • NLP
 • Stanford Research Institute: Shakey
 • Feigenbaum: Expert systems
 • GOFAI / symbolism: operations research / management science; logic-based; knowledge-based / expert systems
 • 
 • 70s: Lighthill report (James Lighthill), big promises -> AI Winter
 • 

90s: Data, Computation, Practical Application -> AI back (90s)
 • 

 • Connectionism optimizations: Geoffrey Hinton: 2006, optimized back propagation
 • 
 • Bloomberg, 2015 was whopper for AI in industry
 • AlphaGo & DeepMind

MLG 002 What is AI, ML, DS

Try a walking desk to stay healthy while you study or work!
 
Show notes at ocdevel.com/mlg/3. 
 
This episode covers four major philosophical topics related to artificial intelligence. The purpose is to give broader context to why AI matters, before moving into technical details in later episodes.
 1. Economic Automation 
AI is automating not just simple tasks like data entry or tax prep, but also high-skill jobs such as medical diagnostics, surgery, and creative work like design, music, and art. There are two common reactions:
 
 • Fear: Concern over job displacement, similar to past economic shifts like the agricultural and industrial revolutions.
 Is your job safe? • Optimism: Automation may lead to more comfortable living conditions and economic structures like Universal Basic Income. New job types could emerge, as they have in past transitions.
 2. The Singularity 
The singularity refers to a point of runaway technological growth, where AI becomes capable of improving itself recursively. This concept is tied to "artificial general intelligence" and "seed AI"—systems that not only perform tasks but create better versions of themselves. The idea is that this could trigger extremely rapid change, possibly representing a new phase of evolution beyond humanity.
 3. Consciousness 
I explore whether consciousness can emerge from machines. Since the brain is a physical machine and consciousness arises from it, it's possible that artificial systems could develop similar properties. Related ideas:
 
 • Qualia: Subjective experiences.
 • Functionalism: If something behaves like it’s conscious, it may be conscious.
 • Turing Test: If a machine is indistinguishable from a human in conversation, it passes the test.
 4. Misaligned Goals and Risk 
I discuss scenarios where AI causes harm not through malevolence but through poorly defined objectives. One example is the "paperclip maximizer" thought experiment, where an AI tasked with maximizing paperclip production might consume all resources to do so. This has led some public figures to raise concerns about AI safety. I don't share the same level of concern, but the topic is worth being aware of.
 References • Ray Kurzweil, 
The Singularity is Near • Ray Kurzweil, 
How to Create a Mind • Daniel Dennett, 
Consciousness Explained • Nick Bostrom, 
Superintelligence • The Great Courses, 
Philosophy of Mind, Brain, Consciousness, and Thinking Machines 
In the next episode, I begin covering the technical foundations of machine learning, starting with supervised, unsupervised, and reinforcement learning.

MLG 003 Inspiration

Try a walking desk to stay healthy while you study or work!
 
Show notes at ocdevel.com/mlg/4
 The AI Hierarchy • Artificial Intelligence is divided into subfields such as reasoning, planning, and learning.
 • Machine Learning is the learning subfield of AI.
 • Machine learning consists of three phases: 
 Predict (Infer) Error (Loss) Train (Learn) • 
 Core Intuition • An algorithm makes a prediction.
 • An error function evaluates how wrong the prediction was.
 • The model adjusts its internal weights (training) to improve.
 Example: House Price Prediction • Input: Spreadsheet with features like bedrooms, bathrooms, square footage, distance to downtown.
 • Output: Predicted price.
 • The algorithm iterates over data, learns patterns, and creates a model.
 • A 
model • = algorithm + learned weights.
 Features • = individual columns used for prediction.
 Weights • = coefficients applied to each feature.
 • The process mimics algebra: rows = equations, entire spreadsheet = matrix.
 • Training adjusts weights to minimize error.
 Feature Types Numerical • : e.g., number of bedrooms.
 Nominal (Categorical) • : e.g., yes/no for downtown location.
 • Feature engineering can involve transforming raw inputs into more usable formats.
 Linear Algebra Connection • Machine learning uses linear algebra to process data matrices.
 • Each row is an equation; training solves for best-fit weights across the matrix.
 Categories of Machine Learning 1. Supervised Learning • Algorithm is explicitly trained with labeled data (e.g., price of a house).
 • Examples: 
 Regression • (predicting a number): linear regression
 Classification • (predicting a label): logistic regression
 • 
 2. Unsupervised Learning • No labels are given; the algorithm finds structure in the data.
 • Common task: 
clustering • (e.g., user segmentation for ads).
 • Learns patterns without predefined classes.
 3. Reinforcement Learning • Agent takes actions in an environment to maximize cumulative reward.
 • Example: mouse in a maze trying to find cheese.
 • Includes rewards (+points for cheese) and penalties (–points for failure or time).
 • Learns policies for optimal behavior.
 • Algorithms: Deep Q-Networks, policy optimization.
 • Used in games, robotics, and real-time decision systems.
 Terminology Recap Algorithm • : Code that defines a learning strategy (e.g., linear regression).
 Model • : Algorithm + learned weights (trained state).
 Features • : Input variables (columns).
 Weights • : Coefficients learned for each feature.
 Matrix • : Tabular representation of input data.
 Learning Path and Structure • Machine learning is a subfield of AI.
 • Machine learning itself splits into: 
 • Supervised Learning
 • Unsupervised Learning
 • Reinforcement Learning
 • 
 • Each category includes multiple algorithms.
 Resources MachineLearningMastery.com • : Accessible articles on ML basics.
 The Master Algorithm • by Pedro Domingos: Introductory audio-accessible book on ML.
 • Podcast’s own curated learning paths: 
ocdevel.com/mlg/resources

MLG 004 Algorithms - Intuition

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Full notes at ocdevel.com/mlg/14
 Anomaly Detection Systems Applications • : Credit card fraud detection and server activity monitoring.
 Concept • : Identifying outliers on a bell curve.
 Statistics • : Central role of the Gaussian distribution (normal distribution) in detecting anomalies.
 Process • : Identifying significant deviations from the mean to detect outliers.
 Recommender Systems Types • : 
 Content Filtering • : Uses features of items (e.g., Pandora’s Music Genome Project).
 Collaborative Filtering • : Based on user behavior and preferences, like "Users Also Liked" model utilized in platforms like Netflix and Amazon.
 • 
 Applications in Machine Learning • : Linear regression applications in recommender systems for predicting user preferences.
 Markov Chains Explanation • : Series of states with probabilities dictating transitions to next states; present state is sufficient for predicting next state (Markov principle).
 Use Cases • : Often found in reinforcement learning and operations research.
 Monte Carlo Simulation • : Running simulations to determine the expected value or probable outcomes of Markov processes.
 Resource Andrew NG's Coursera Course - Week 9 • : Focuses on anomaly detection and recommender systems.

MLG 014 Shallow Algos 3

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