by Albert Major
Example:
A weather-predicting algorithm first assumes 50% rain chance.
After observing data (humidity, temperature), it adjusts to make better predictions.
*This slide defines machine learning as when an agent—in this case, a computer or algorithm—improves task performance by making observations about the world. It builds a model from data, which acts both as a hypothesis about the world and a piece of software that solves problems.*
Reminder, from Semester 1: Inductive learning means reasoning from specific examples to general rules, while deductive learning means reasoning from general rules to specific conclusions.
| Deductive Learning | Inductive Learning (Machine Learning) |
|---|---|
| Uses rules to derive conclusions | Learns patterns from data |
| Requires prior knowledge | Requires only a dataset, but no prior knowledge |
| Conclusions are guaranteed (if rules are sound) | Conclusions are approximate, not guaranteed |
This slide contrasts *deductive learning* (not ML) and *inductive learning* (ML). Deductive learning relies on *prior knowledge* and produces *guaranteed correct* conclusions if the rules are sound. In contrast, machine learning is *inductive*: it requires *no prior knowledge*, learns *directly from data*, and produces *approximate* conclusions without guaranteed correctness.
so, what does it look like?
Think of a matrix (table):
This slide shows how a machine learning problem is structured: the *input* is a vector of *features* or *attributes*, and the *output* is either a *finite label* (*classification*) or a *numerical value* (*regression*). This setup defines how data is represented in ML and what kind of prediction task is being solved.
This slide shows a typical *machine learning dataset* with *input features* (subject, time, day) and a *finite output label* (yes/no), used for a *classification* task. Each row is an *example*, and the goal is to learn patterns to predict the output for new combinations of inputs.