# Lecture 11 – Introduction to Modeling

Presented by Suraj Rampure

Content by Suraj Rampure, Ani Adhikari, Deborah Nolan, Joseph Gonzalez

A reminder – the right column of the table below contains *Quick Checks*. These are **not** required but suggested to help you check your understanding.

Video | Quick Check | |
---|---|---|

11.1 Defining a model. |
11.1 | |

11.2 Choosing the constant model. Formalizing the notion of a parameter. |
11.2 | |

11.3 Loss functions and their purpose. Squared loss and absolute loss. Minimizing average loss (i.e. empirical risk). |
11.3 | |

11.4 Minimizing mean squared error for the constant model using calculus, to show that the sample mean is the optimal model parameter in this case. |
11.4 | |

11.5 Performing the same optimization as in the last video, but by using a non-calculus algebraic manipulation. |
11.5 | |

11.6 Minimizing mean absolute error for the constant model using calculus, to show that the sample median is the optimal parameter in this case. Identifying that this solution isn't necessarily unique. |
11.6 | |

11.7 Comparing the loss surfaces of MSE and MAE for the constant model. Discussing the benefits and drawbacks of squared and absolute loss. Recapping the "modeling process". |
11.7 |