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Support Vector Machine (SVM) Theory

In this tutorial, we'll describe the mathematics behind Support Vector Machines. This tutorial is going to be much more math heavy as compared to other tutorials. If you're mostly interested in applying SVMs to solve problems, then our first tutorial on SVMs is sufficient. However, if you would like to understand the mathematical basis of Support Vector Machines, then you'll find this tutorial interesting.

In this tutorial, we will focus on the hard-margin SVM and soft-margin SVM. However, we will not be considering kernels or the hyper-parameter \gamma (gamma).

Problem Formulation

In SVM, the decision function for predicting data points x corresponds to the equation of an hyperplane:

f_w(x) = w^T x + w_0

Dropout algorithm

Dropout is a regularization technique where during each iteration of gradient descent, we drop a set of neurons selected at random. By drop, what we mean is that we essentially act as if they do not exist.

What is Bioinformatics?

What is a DNA? What is a Protein?

This section (single tutorial) provides the biological context for the rest of this course. It describes what DNAs and proteins are, and how we can model them as a sequence of characters.

DNA and Protein: What they are, and what role do they play?
Free
11 mins

Conserved Sequences and Regulatory Motifs with Brute-Force Algorithms and Randomized Algorithms

Conserver sequences and regulatory motifs are short sequences (say of length 15-30) which occur frequency in the genome. These sequences serve a variety of functions - such as regulating gene expression (and hence how much of a protein is produced) and indicating where genes begin. We'll see how to find these sequences using brute-force algorithms and randomized algorithms.

Genome Assembly and Sequence Alignment with Graph Algorithms and Dynamic Programming

Recently, we've figured out cost effective ways of sequencing a human genome, i.e. taking a human genome and reading sequence of 3-4 billion nucleic acids (A, C, G and T) that it comprises of. In this section, we'll see how this is achieved. We'll also see how we can find similar regions in two different genomes, which allows us to do things like infer evolutionary history and predict protein function.

Start this course

DNA and Protein: What they are, and what role do they play?

Category: Machine Learning

1 Comment

Top comment in this discussion

Martins Nkume42w

I am delighted to begin this course.