Support Vector Machine (SVM) – Optimization objective So far, we’ve seen a range of different algorithms With supervised learning algorithms – performance is pretty similar What matters more often is; The amount of training data Skill of applying algorithms One final supervised learning algorithm that is widely used – support vector machine (SVM) Compared Read more…
Machine learning systems design In this section we’ll touch on how to put together a system Previous sections have looked at a wide range of different issues in significant focus This section is less mathematical, but material will be very useful non-the-less Consider the system approach You can understand all Read more…
Deciding what to try next We now know many techniques But, there is a big difference between someone who knows an algorithm vs. someone less familiar and doesn’t understand how to apply them Make sure you know how to chose the best avenues to explore the various techniques Here we focus deciding Read more…
Neural network cost function NNs – one of the most powerful learning algorithms Is a learning algorithm for fitting the derived parameters given a training set Let’s have a first look at a neural network cost function Focus on application of NNs for classification problems Here’s the set up Training Read more…
Neural networks – Overview and summaryWhy do we need neural networks? Say we have a complex supervised learning classification problem Can use logistic regression with many polynomial terms Works well when you have 1-2 features If you have 100 features e.g. our housing example 100 house features, predict odds of Read more…
The problem of overfitting So far we’ve seen a few algorithms – work well for many applications, but can suffer from the problem of overfitting What is overfitting? What is regularization and how does it help Overfitting with linear regression Using our house pricing example again Fit a linear function Read more…
Classification Where y is a discrete value Develop the logistic regression algorithm to determine what class a new input should fall into Classification problems Email -> spam/not spam? Online transactions -> fraudulent? Tumor -> Malignant/benign Variable in these problems is Y Y is either 0 or 1 0 = negative class (absence of something) Read more…
Linear regression with multiple features Multiple variables = multiple features In original version we had X = house size, use this to predict y = house price If in a new scheme we have more variables (such as number of bedrooms, number floors, age of the home) x1, x2, x3,x4 are the four features Read more…
Matrices – overview Rectangular array of numbers written between square brackets 2D array Named as capital letters (A,B,X,Y) Dimension of a matrix are [Rows x Columns] Start at top left To bottom left To bottom right R[r x c] means a matrix which has r rows and c columns Is a [4 Read more…
Introduction to the course We will learn about State of the art How to do the implementation Applications of machine learning include Search Photo tagging Spam filters The AI dream of building machines as intelligent as humans Many people believe best way to do that is mimic how humans learn What the course covers Learn about state Read more…