PCA Exercise 2

The second exercise for principle components analysis. The associated code is in the PCA Ex2.R file. Interested students are encouraged to replicate what we go through in the video themselves …

PCA Exercise 1

The first code exercise for principle components analysis. The associated code is in the PCA Ex1.R file. Interested students are encouraged to replicate what we go through in the video …

Principal Components Analysis

Principal components analysis (PCA) is a linear dimensionality reduction technique that will produce linear mixtures of the original input features from which a small subset of highly informative features can …

Summary for Week Four

That’s it! You’ve made it through the entire Advanced Machine Learning course! We will have a little course conclusion article after the last weekly test, but in this step we …

Course Conclusion

Congratulations! You have finished the Advanced Machine Learning course! Having completed this course, you should be able to: Explain the steps of a typical data science problem, and perform those …

Multi-Armed Bandit Optimization

Multi-Armed Bandit Optimization The phrase ‘multi-armed bandit’ comes from the description/nickname ‘one-armed bandit’ which refers to slot machines, each of which has a single ‘arm’ which is pulled to run …

Advanced Machine Learning: Laplacian Regularized Least Squares

Semi-Supervised Learning Recap Semi-supervised learning deals with cases similar to supervised learning except that we do not have labels (values for the target variable) for all our training data. Typically, …

Missing Data: Missing Value Imputation Exercise 2 (MCMC)

A code exercise for using the Metropolis in Gibbs MCMC algorithm to impute missing data values. A code exercise for using the EM algorithm to impute missing data values. The …

Missing Data: Missing Value Imputation Exercise 1 (EM)

A code exercise for using the EM algorithm to impute missing data values. The associated code is in the Missing Data Ex1.R file. Interested students are encouraged to replicate what …

Missing Data Basics

We now turn to the problem of missing data, and solutions to this problem. Missing data occurs when there are values in your dataset that are missing, or unknown. This …

Advanced Machine Learning: Missing Value Imputation

Missing Data Imputation When data is missing at random the best option is often to impute, or estimate, the missing values. We have already seen two powerful approaches that can …

Feature Engineering: Introduction

We know that unnecessary complexity in models leads to adverse effects, in that it requires more data to avoid overfitting as well as causing greater computational complexity. One cause of …

Introduction for Week 4

Welcome to the final week of the Advanced Machine Learning course. No video introduction this week, I am afraid – though it saves me looking at myself, which I am …

LDA Exercise 1

A video exercise for latent Dirichlet analysis. The associated code is in the LDA Ex1.R file. Interested students are encouraged to replicate what we go through in the video themselves …

How to Use Metropolis within Gibbs MCMC for Latent Dirichlet Analysis

Topic Modeling Topic modeling is a form of unsupervised learning that seeks to categorize documents by topic. Typically the topics are considered to be latent variables and as such are …