MachineLearning:Course:Basic

lastmod 2026-07-31created 2018-07-1919 edits

An explanation of machine learning.

About

Machine learning is a branch of artificial intelligence: the field of developing algorithms and techniques that allow computers to learn.

The field of study that gives computers the ability to learn, that is, the ability to perform behavior that is not defined in code

Required foundational theory

• Statistics
• Neuroscience
• Graph theory
• Probability theory
• Bayesian theory (Bayesian probability): an interpretation of probability theory that treats probability as 'a measure of a state of knowledge'.

Algorithm types

• Supervised Learning
  • Regression problem (continuous)
  • Classification problem (non-continuous)
• Unsupervised learning
  • Clustering
• Semi-supervised learning
• Reinforcement learning
• Deep learning

Model Representation

(He raised his middle finger, the training began, and a model was born) [^0]

A typical machine learning concept diagram looks like the one above.

• Input Features: input variables
• Training Data-Set: training variables
• Learning Algorithm
  • Cost function: the method for finding the Parameter(θ).
• Hypothesis: the function relating the predicted data and the result values
• Expected Output: the result of Inferencing with the Hypothesis.

See also

• Machine learning