Introductory Applied Machine Learning
Eighteen playlists of tutorial videos created for the online distance learning postgraduate course – Introductory Applied Machine Learning.
The course is about the principled application of machine learning techniques to extracting information from data. The main area discussed is supervised learning, which is concerned with learning to predict an output, given inputs. A second area of study is unsupervised learning, where we wish to discover the structure in a set of patterns, i.e. there is no output “teacher signal”. The primary aim is to provide the student with a set of practical tools that can be applied to solve real – world problems in machine learning, coupled with an appropriate, principled approach to formulating a solution.
View the Introductory Applied Machine Learning playlists directly on Media Hopper Create
Linear Regression V1
Maths and Probability
Thinking about data
Naive Bayes
Decision Trees
Generalisation and Evaluation
Linear Regression
Logistic Regression
Optimisation
Regularisation
SVM Part 1
SVM Part 2
Nearest Neighbours
K-Means
Gaussian Mixture Models
Principal Components Analysis
Hierarchical Clustering
Neural Networks
These videos were created by Dr Nigel Goddard © The University of Edinburgh, and are licensed under a Creative Commons Attribution licence.
Header Image by mohamed Hassan from Pixabay
