Module 2

Link to Notebooks for Sections¶

Below you will find the Jupyter Notebooks for the Friday sections. These are Python exercises that you must work on with your group and submit as a grade.

Link to Notebooks for Lectures¶

Below you will find the Jupyter Notebooks for the Monday/Wednesday lectures. These are supplementary notebooks that explore topics in the lectures. The examples shown in the lectures are implemented in these notebooks in addition to other content. These notebooks will be helpful for - understanding content in the lecture - completing computational homework exercises - completing the section notebooks (graded)

Mod2-Lec1¶

In this notebook we explore “Least squares approximate solutions” to

$\begin{aligned} Ax&=b \end{aligned}$

That is solutions to the optimization problem

$\begin{aligned} \min_x\|Ax-b\|_2^2 \end{aligned}$

• notebook link

Mod2-Lec2¶

In this notebook we explore data fitting in ML more generally.

• notebook link
• Housing data is used in Mod2-Lec2 and can be found here: housing data

Mod2-Lec3¶

In this notebook, we explore “Least Squares Classification”

• notebook link
• Iris data set is used in Mod2-Lec3 and is stored as a pickle file. It can be found here: iris data
• You will also use the confusion matrix creator located in cf_matrix.py which can be found here

can load data using the command irisD=pk.load(open('./data/iris_data.p','rb')) where you have stored the pickle file iris_data.p in your current directory in the folder data.

Mod2-Lec4¶

In this notebook, we explore “Kernel Regression”

• notebook link