And how to improve UMAP. This is the thirteenth article of my column Mathematical Statistics and Machine Learning for Life Sciences where I try to explain some mysterious analytical techniques used in Bioinformatics, Biomedicine, Genetics etc. in a simple way. In the previous post How Exactly UMAP works I started with an intuitive explanation of the math behind UMAP. The best way to learn it is to program UMAP from scratch, this is what we are going to do today. The idea of this post is to show that it is relatively easy for everyone to create their own neighbor graph dimension reduction technique that can provide even better visualization than UMAP. It is going to be lots of coding, buckle up!
Sonar is commonly used to map the ocean floor, and seabed composition (e.g. Salinity, depth and water temperature also affect how sound waves are propagated through water. This means that sonar measurements at different depths and distances can give accurate soundings of the ocean's properties, for example how underwater currents propagate, how the deeper ocean changes with the climate or where best to listen to whales. Working with Systems Engineering & Assessment Ltd (SEA), scientists at the University's Institute for Mathematical Innovation (IMI) have developed an Artificial Intelligence (AI) algorithm which could improve underwater mapping by making sense of incomplete data and working out how many measurements are needed to give an accurate survey. The research was part of a project contracted by The Defence and Security Accelerator (DASA), a part of the Ministry of Defence, to improve monitoring of the UK's vast marine territories using high tech sonar.
Machine learning, the subset of artificial intelligence that teaches computers to perform tasks through examples and experience, is a hot area of research and development. Many of the applications we use daily use machine learning algorithms, including AI assistants, web search and machine translation. Your social media news feed is powered by a machine learning algorithm. The recommended videos you see on YouTube and Netflix are the result of a machine learning model. And Spotify's Discover Weekly draws on the power of machine learning algorithms to create a list of songs that conform to your preferences. But machine learning comes in many different flavors.
Kernel methods, a new generation of learning algorithms, utilize techniques from optimization, statistics, and functional analysis to achieve maximal generality, flexibility, and performance. These algorithms are different from earlier techniques used in machine learning in many respects: For example, they are explicitly based on a theoretical model of learning rather than on loose analogies with natural learning systems or other heuristics. They come with theoretical guarantees about their performance and have a modular design that makes it possible to separately implement and analyze their components. They are not affected by the problem of local minima because their training amounts to convex optimization. In the last decade, a sizable community of theoreticians and practitioners has formed around these methods, and a number of practical applications have been realized.
This article will discuss what I consider to be the three levels of data science competency, namely: level 1 (basic level); level 2 (intermediate level); and level 3 (advanced level). Competency increases from level 1 to 3. We shall use Python as the default language, even though other platforms such as R, SAS, and Matlab could be used as programming languages for data science. The views provided here are my views and are based on my own journey to data science. At level one, a data science aspirant should be able to work with datasets generally presented in comma-separated values (CSV) file format. They should have competency in data basics; data visualization; and linear regression.
Diabetes management is a difficult task for patients, who must monitor and control their blood glucose levels in order to avoid serious diabetic complications. It is a difficult task for physicians, who must manually interpret large volumes of blood glucose data to tailor therapy to the needs of each patient. This paper describes three emerging applications that employ AI to ease this task: (1) case-based decision support for diabetes management; (2) machine learning classification of blood glucose plots; and (3) support vector regression for blood glucose prediction. The first application provides decision support by detecting blood glucose control problems and recommending therapeutic adjustments to correct them. The second provides an automated screen for excessive glycemic variability.
Given the problem you want to solve, you will have to investigate and obtain data that you will use to feed your machine. The quality and quantity of information you get are very important since it will directly impact how well or badly your model will work. You may have the information in an existing database or you must create it from scratch. If it is a small project you can create a spreadsheet that will later be easily exported as a CSV file. It is also common to use the web scraping technique to automatically collect information from various sources such as APIs.
Here, we load the chocolate data into our program using pandas; we also drop two of the columns we won't be using in our calculation: competitorname and winpercent. Our y becomes the first column in the dataset which indicates if our specific sweet is chocolate (1) or not (0). The remaining columns are used as variables/features to predict our y and, thus, become our X. If you're confused about why we're doing with …[:, 0][:,np.newaxis] on line 5, this is to turn y into a column. We simply add a new dimension to convert the horizontal vector into a vertical column!
There has been a lot of talk about making machine learning more explainable so that the stakeholders or the customers can shed the scepticism regarding the traditional black-box methodology. So, in order to find out how it is being implemented, a group of researchers conducted a survey. In the next section, we look at a few findings and practices for deploying as recommended by the researchers at Carnegie Mellon University, who published a work in collaboration with top institutes. During their survey, the researchers have come across some concerns such as model debugging, model monitoring and transparency among many others during the interviews that they have conducted with organisations as part of their work. The study found that most data scientists struggle with debugging poor model performance.
Linear algebra is to machine learning as flour to bakery: every machine learning model is based in linear algebra, as every cake is based in flour. It is not the only ingredient, of course. Machine learning models need vector calculus, probability, and optimization, as cakes need sugar, eggs, and butter. Applied machine learning, like bakery, is essentially about combining these mathematical ingredients in clever ways to create useful (tasty?) models. This document contains introductory level linear algebra notes for applied machine learning. It is meant as a reference rather than a comprehensive review. It also a good introduction for people that don't need a deep understanding of linear algebra, but still want to learn about the fundamentals to read about machine learning or to use pre-packaged machine learning solutions. Further, it is a good source for people that learned linear algebra a while ago and need a refresher. These notes are based in a series of (mostly) freely ...