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: 2022-23
See module specification for other years: 2021-22

Module summary

Machine Learning and Optimisation

Module will run

Occurrence	Teaching period
A	Spring Term 2022-23 to Summer Term 2022-23

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
Online Exam -less than 24hrs (Centrally scheduled) Intelligent Systems 2 (INT2) Exam	3 hours	50
Open Examination: Multiple choice questions online INT2 Open Assessment (Groupwork)	N/A	35
Open Examination: Multiple choice questions online INT2 Open Assessment (Void Self Assessment)	N/A	15

Special assessment rules

None

Reassessment

Task	Length	% of module mark
Online Exam -less than 24hrs (Centrally scheduled) Intelligent Systems 2 (INT2) Exam	3 hours	50
Open Examination: Multiple choice questions online INT2 Open Assessment (Groupwork)	N/A	35
Open Examination: Multiple choice questions online INT2 Open Assessment (Void Self Assessment)	N/A	15

Module feedback

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

Indicative reading

Solomon, Justin. Numerical Algorithms. AK Peters/CRC Press, 2015

Studying at York

Intelligent Systems 2: Machine Learning & Optimisation - COM00024I

Module summary

Module will run

Module aims

Module learning outcomes

Assessment

Special assessment rules

Reassessment

Module feedback

Indicative reading

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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