Faisal Qureshi

http://www.vclab.ca

- What is Machine Learning (ML)
- ML tasks
- ML and deep learning

Figure from the Scientist

- Mitchell, 1997:
A computer program is said to learn from experience $E$ with respect to some class of tasks $T$ and performance measure $P$ , if its performance at tasks in $T$ , as measured by $P$ , improves with experience $E$.

- "Subfield of artificial intelligence"
- More instructive: Machine learning is a means of
*building mathematical models of data*.

- "
*Given input $A$, predict target $B$*" - Entails
*regression*(*continuous*labels/targets) &*classification*(*discrete*targets/labels) - Using established data measurements to infer targets/labels in as-of-yet unseen data
- Prediction accuracy can be evaulated from prescribed data

- "
*Letting the dataset speak for itself*" - Entails
*clustering*,*dimensionality reduction*, and*density estimation* - "
*What patterns can we infer from $A$? Which features of $A$ occur together frequently?*"

- Inputs
*features*$\mathbf{X}$ and generic (continuous)*targets*/*labels*

**Desired output**

- Inputs
*features*$\mathbf{X}$ and*categorical*/*discrete**targets*/*labels*

**Desired output**

- Dimensionality reduction
- Fitting distributions
- Recommender systems
- Association analysis

- model --> network, graphs
- parameters --> weights
- fitting --> learning
- test set performance --> generalization
- regression, classification --> supervised learning
- density estimation, dimensionality reduction, clustering --> unsupervised learning
- large grant ~50,000 dollars --> large grant ~1,000,000 dollars

Credit: https://xkcd.com/1838/

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