**References**

**Notes** {{word-count}}

**Summary**:

**Key points**:

What is Machine Learning?

In Machine Learning, instead of defining the input -> output relationship by hand, we learn a program that acquires the relationship from data.

Tom Mitchell's definition

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 $E$.

The Machine Learning method for solving any problem ever.

Define your model class.

Model class refers to the the set of possible programs。

Define your Loss Function.

Loss Function measures if one model in the model class is better than another.

Define your Optimizer.

The optimizer searches the model class to find the model that minimizes the Loss Function.

Run it on a GPU.

Marr's levels of analysis

What is learning?

This is called the Maximum Likelihood Estimation (MLE), and it can be formulated as a Negative Log-Likelihood (NLL) problem.

In Machine Learning, instead of defining the input -> output relationship by hand, we learn a program that acquires the relationship from data.

Deep Learning is Machine Learning with **multiple layers** of **learned representations**.

The Machine Learning method for solving any problem ever.