mirror of
https://github.com/jackyzha0/quartz.git
synced 2025-12-27 14:54:05 -06:00
65 lines
1.4 KiB
Markdown
65 lines
1.4 KiB
Markdown
---
|
|
title: "17-ML-in-IA-1"
|
|
aliases:
|
|
tags:
|
|
- comp210
|
|
- lecture
|
|
sr-due: 2022-09-25
|
|
sr-interval: 3
|
|
sr-ease: 250
|
|
---
|
|
|
|
|
|
traditional approaches
|
|
- hand crafted and curated
|
|
- based on intuition not evidence (e.g., password criteria example)
|
|
- static and difficult to scale and adapt
|
|
|
|
Everything we do generates data. This can be used to learn what works and what doesn't
|
|
|
|
We use the data to evolve from observations to models
|
|
|
|
data doesn't lie. humans do (sometimes using data)
|
|
|
|
ai uses machine learning, data science applies machine learning. statistical learning is machine learning
|
|
|
|
# ML
|
|
using mathematics and algorithms to build models from data for prediction and insight
|
|
|
|
key concepts
|
|
- getting dta
|
|
- feature enginering
|
|
- model testing/validation
|
|
- knowledge extraction
|
|
|
|
supervised vs unsupervised learning — often feed into each other
|
|
|
|
|
|
|
|
## feature engineering
|
|
converting domain variables into actionable features
|
|
|
|
e.g.,
|
|
- edges from images
|
|
- word frequencies in documents
|
|
- converting TCP stream into counts of particular packet types
|
|
|
|
|
|
## insights
|
|
modelling uncovers relatonships within inderlying process represented by the data
|
|
|
|
e.g., detecting a password attack
|
|
|
|
benefits
|
|
- provide insight
|
|
- automattion
|
|
- adapt to changes
|
|
- scale larger and more complex problems
|
|
|
|
# threats
|
|
|
|
|
|
need to secure all of them - smartly
|
|
|
|
|