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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:
  1. Predict (Infer)
  2. Error (Loss)
  3. 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