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Week 7: Matrices Part 2
No full textIn this unit we continue with our work on matrices. We describe how to calculate the determinant of a 2 x 2 matrix and introduce the condition for the existence of an inverse matrix. A formula for calculating the inverse of a 2 x 2 matrix is presented supported by examples. Some applications of matrices in the real-world are then given, including solving linear systems of algebraic equations, computer graphics, cryptography and the modelling of graphs and networks
MSBE, week 5, Morna Lawson (GSBS Learning Development Centre)
No full textA talk about the requirements for 4th year academic writing
Web Platform Development 2: Week 3 - Mustache
No full textIs a template system that, when rendered...
substitutes mustache tags (mustaches, {{ }}) in a template with values provided by a data object or HashMap.
A mustache template is a string (such as an HTML page) that contains any number of mustache tags
Big Data: Week 9 - Data Analysis Recap
No full textIn the last three weeks, we have talked about data exploring, data pre-processing and data analysis. In the data exploring stage, we use approaches such as visual exploration or statistics to understand what is in a dataset and the characteristics of the data. These characteristics can include size or amount of data, completeness of the data (e.g., the number of missing values), correctness of the data (e.g., outliers), possible relationship amongst data elements or variables in the data (e.g., correlations, distribution).
Based on the information obtained through data exploring, data pre-processing aims to improve the quality of data by dealing with missing values, removing unusable parts of the data, correcting poorly formatted elements and defining relevant relationships across datasets. Common approaches for data pre-processing include missing value imputation, data scaling, normalization, Feature selection and Dimension reduction.
After data exploring and data pre-processing, data are prepared for analysis. Machine learning algorithms are widely used in data analysis. There are various machine learning algorithms, which can be grouped into different types. Supervised learning and unsupervised learning are two major types of machine learning. In this module, we introduced three supervised machine learning algorithms, i.e., Linear regression, SVM (Support Vector Machine) and NN (Neural Network), and one unsupervised machine learning algorithm: K-means Clustering
C Reactive Protein
No full textVideo overview of the C-Reactive Protein test. The video is used as a component part of teaching for podiatry students at Glasgow Caledonian University
Blood Glucose and HBA1c
No full textVideo overview of the blood glucose and HBA1c test. The video is used as a component part of teaching for podiatry students at Glasgow Caledonian University
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