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- Machine learning is a subset of AI.
- Simplistic description: a pattern recognition system.

- Probability and Statistics: used for use data sample to do:
- Inference
- Prediction
- Clustering ?
- Classification

- optimization
- example
*y = mx + b*- in machine learning, you are trying to find the best answer for
*m* - b is used to account for obscurities and bias in the model
- x is the input
- y is the result.

- in machine learning, you are trying to find the best answer for

- example
- two main ways of learning
- Discriminative model
- decision boundary
- uses:
- Regressions
- Support Vector Machines

- most deep neural nets are discriminative models

- Generative model
- probability distributions of the data set
- uses:
- Naive Bayes (https://en.wikipedia.org/wiki/Bayes%27_theorem)
- Generative Adversarial Network

- Discriminative model
- What ML can't do
- learn without sufficient data
- explain itself
- reason
- cannot understand that if a < b and b < c, that a < c

- "Do the right thing" that is change break from the task to do something that is important but out of "character" e.g. a protestor stopping to help an injured police officer.

- What ML can do
- Repetitive tasks really fast
- Drive you crazy (takes a lot of effort to make your model work correctly)
- Generate synthetic data
- including pictures, video, art, poems, etc.
- Example: https://www.thispersondoesnotexist.com/
- Wikipedia article about the above site and the GAN network it uses: https://en.wikipedia.org/wiki/StyleGAN

- Question about the machine not being able to explain what it is doing.
- Discussion about this question brought up this link:
- https://cacm.acm.org/magazines/2020/1/241703-techniques-for-interpretable-machine-learning/fulltext

- Bias vs Variance tradeoff
- to minimize error rates you want to minimize the bias and variance.
- less bias has more variance
- less variance has more bias

- to minimize error rates you want to minimize the bias and variance.