Extracting information from the unprecedented amount of data (aka. big data) that has been collected in recent years is a very important task in science and engineering, with great social and economical impact. Machine learning addresses the problem of how computers can learn and extract information automatically from data, and it is behind many methods used in artificial intelligence, data mining or adaptive system design. It is widely applied in practice in most disciplines where data is available, including, e.g., electrical engineering, computer science, or medicine. The aim of this module is to introduce you to the theory and practice of modern machine learning methods.

Upon successful completion of this module, you will be able to:
1. Develop solutions to machine learning problems by modelling and pre-processing data, and designing, selecting and develop appropriate learning algorithms.
2. Consider and contrast the problems of learning and overfitting in an ML system
3. Jutsify the use of linear regression, classification, logistic regression, support vector machines, neural networks, nearest neighbour and clustering.
4. Recommend and construct the use of a machine learning algorithm in unseen situations.

Part 1. Components of learning, tasks, types of learning, ML problem formulation,simple predictors
Part 2. Feasibility of learning, error function, Empirical Risk Minimization, generalisationbounds, performance vs complexity, bias/variance trade off, Hoeffding/VC inequalities
Part 3. Feature transformations, noisy data, overfitting, regularisation
Part 4. Logistic regression, gradient descent, Perceptron, Multi Layer Perceptron,Neural Network, backpropagation
Part 5. Hyperplane, separation with hard margin, soft margin, support vector machines,
Part 6. Nearest neighbour classification, linear unsupervised learning, principlecomponent analysis
Part 7. K-means clustering, kernel K-means, advanced clustering algorithms