Anomaly detection – problem motivation Anomaly detection is a reasonably commonly used type of machine learning application Can be thought of as a solution to an unsupervised learning problem But, has aspects of supervised learning What is anomaly detection? Imagine you’re an aircraft engine manufacturer As engines roll off your assembly line you’re doing QA Read more…
Motivation 1: Data compression Start talking about a second type of unsupervised learning problem – dimensionality reduction Why should we look at dimensionality reduction? Compression Speeds up algorithms Reduces space used by data for them What is dimensionality reduction? So you’ve collected many features – maybe more than you need Can you “simply” Read more…
Unsupervised learning – introduction Talk about clustering Learning from unlabeled data Unsupervised learning Useful to contras with supervised learning Compare and contrast Supervised learning Given a set of labels, fit a hypothesis to it Unsupervised learning Try and determining structure in the data Clustering algorithm groups data together based on data features What Read more…
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…