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# Intelligent Systems 2: Machine Learning & Optimisation - COM00024I

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• Department: Computer Science
• Module co-ordinator: Dr. Patrik Huber
• Credit value: 20 credits
• Credit level: I
• Academic year of delivery: 2021-22

## Module summary

Machine Learning and Optimisation

## Module will run

Occurrence Teaching cycle
A Spring Term 2021-22 to Summer Term 2021-22

## Module aims

This module advances the IS stream by introducing the basics of machine learning, purely from an optimisation perspective. The range of topics covers linear regression (picking up where Data 1 left off) to decision trees and a simple neural network (leading into advanced machine learning in the third year). Understanding ML requires knowledge of some mathematical concepts that build upon A-level standard mathematics, specifically: Linear Algebra and Continuous Optimisation. This will be taught in-place. Students will see motivating real world problems, the ML techniques required to solve them, the underlying mathematics needed for the technique and their practical implementation. Practicals will be taught using a python-based modern machine learning library such as TensorFlow or PyTorch and so students will gain experience with the declarative programming paradigm (building on the Software stream).

## Module learning outcomes

 I201 Define the machine learning paradigm and distinguish it from the wider field of AI I202 Distinguish supervised from unsupervised learning, parametric from nonparametric learning and classification from regression. Be able to express a classification problem as a regression problem. I203 Compute partial derivatives and understand the concept of the gradient as a generalisation of the derivative I204 Express, manipulate and solve systems of linear equations using linear algebra I205 Apply linear regression and logistic regression with one variable or multiple variables I206 Optimise multivariate functions using gradient descent I207 Explain the concept of overfitting and how regularisation can be used to prevent it I208 Construct a basic neural network and learn its weights via optimisation using the backpropagation algorithm I209 Identify clusters in data by applying the unsupervised k-means algorithm I210 Use K-nearest neighbours and decision tree learning for nonparametric classification I211 Use a modern machine learning library to implement basic machine learning methods as a computational graph and apply them to real datasets I212 Appreciate the ethical and privacy implications of machine learning such as relying on black box systems without interpretable parameters, learning from biased or crawled publicly available data

## Assessment

Task Length % of module mark
Groupwork
INT2 Open Assessment
N/A 50
Online Exam
INT2 Closed Exam
8 hours 50

None

### Reassessment

Task Length % of module mark
Groupwork
INT2 Open Assessment
N/A 50
Online Exam
INT2 Closed Exam
8 hours 50

## Module feedback

Feedback is provided through work in practical sessions, and after the final assessment as per normal University guidelines