### Definition > Field of study that gives the computers the ability to learn without explicitly programmed. -Arthur Samuel 1959 - He made the software that played **checkers**. Eventually, the program played better than himself. ### Algorithm Types #### 1. Supervised Learning - Learns from given a sample of **right answers**. - **used the most** in real-life applications and has seen rapid development. Examples: ![[Pasted image 20250316190734.png]] --- Algorithms: 1. **Regression**: Fit a line or curve (function) on a set of data: *prediction* 2. **Classification**: To identify/classify an input to limited outcomes. > [!tip] > In *regression* the result is a **continuous** data. Whereas in *classification* it is a set of finite **category**. >[!info] > Both in regression and classification, we can have **multiple input value**. #### 2. Unsupervised Learning - Input data is **not** labeled in unsupervised learning. - We look for any interesting thing about the input data like **structures** and **patterns**. 1. **Clustering**: e.g. Google News: groups similar news together. e.g. genetic DNS microarray clustering. ![[Pasted image 20250316192608.png]] - Classification vs. Clustering 2. **Anomaly Detection**: find unusual data points. e.g. fruad 3. **Dimensionality reduction**: compress data using fewer numbers.