## Welcome to the Machine Learning meetup!

Todays agenda:

18:15 - 18:45: Introduction to clustering and feature engineering

Short break, refill drinks, greet & meet etc.

19:00 - 19:30: Introduction to data (mining) analysis in Python

19:30: Hang around and discuss clustering and machine learning.

# Feature Engineering

Our dataset $$X = \begin{pmatrix} x_1 \\ x_2 \\ x_3 \\ x_4 \\ \vdots \\ x_n \end{pmatrix}$$

Each sample $$x_1 = \begin{pmatrix} f_1, & f_2, & f_3, & f_4, & \dots, & f_n \end{pmatrix}^T$$

# Features: The words? The datetime? The font used?

$$X = \begin{pmatrix} document_1 \\ document_2 \\ document_3 \\ document_4 \\ \vdots \\ document_n \end{pmatrix} , \: Y = \begin{pmatrix} subject_1 \\ subject_2 \\ subject_1 \\ subject_3 \\ \vdots \\ subject_k \end{pmatrix}$$

# Stop when converge

$$\underset{\mathbf{S}} {\operatorname{arg\,min}} \sum_{i=1}^{k} \sum_{\mathbf x \in S_i} \left\| \mathbf x - \boldsymbol\mu_i \right\|^2$$

# Silhouette score

$$a(x) = \text{average distance to all other sample in the SAME cluster}$$

$$b(x) = \text{average distance to the samples of the closest cluster}$$

$$s(x) = \frac{b(x) - a(x)}{\max\{a(x),b(x)\}}, \forall x\in X$$

$$-1 \le s(x) \le 1$$

# Recommender systems

• Discovery - new music
• Related artists

# Clustering

• User behavior
• Artist disambiguation ( the kent bug )

## Stay put, we will soon start again!

18:15 - 18:45: Introduction to clustering and feature engineering

Short break, refill drinks, greet & meet etc.

19:00 - 19:30: Introduction to data (mining) analysis in Python

19:30: Hang around and discuss clustering and machine learning.