University of the Philippines Manila (UPM) invited us to talk about our research in Bioinformatics. I will discuss our research on visualization of yeast gene expression data.

# Category Archives: Machine Learning

# Machine Learning Mini Project

# Review of Linear Algebra for Machine Learning

Data is often represented by vectors with d scalar features

**Definition 1.** The **inner product** of two vectors x and y is

**Definition 2. **

The** Euclidean Norm **of vector x is ||x||

Note that

If ||x|| = 1, then x is **normalized**

Otherwise, we can normalize x to x’ as follows

**Definition 3.**

Vector x and y are **orthogonal,** if their scalar product is zero.

The angle between two vectors x and y is if

Geometric interpretation is shown below

Note that the orthogonal projection of y onto x is

# Introduction to Machine Learning

**Machine Learning**

is useful when

- the pattern exists.
- it is difficult to pin down the problem mathematically.
- you have a data.

Patternis an entity vaguely defined that could be given a name

–Watanabe

Examples of patterns are palindromes in a sequence, spatial configuration of pixels in character recognition, speech signal in spectrogram, the salary, age, and debt records in credit card applications.

**Learning** is a process by which parameters of a learning machine are modified through a continuous process of stimulation by the environment in which it is embedded.

In Figure 1, parameters of the learning machine are tweaked based from the error signal.

### Learning Paradigm

- Supervised Learning -with the help of a teacher
- Unsupervised Learning – with the help of a critic

Why unsupervised learning is important?

It is important because it may lead to a new pattern, thus leading to knowledge discovery.