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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