2nd year Maths Module for Data Science. This Module will take a computational approach to explore the mathematical topics required for Machine Learning
Computation for Mathematics
Introduction to programming/scripting for computational MathematicsIntroduction to Mathematical Libraries and functions
Analytical Geometry
Co-Ordinate Geometry, Distance , Plots etc
Matrices
Matrix Operations, Determinants, Solution of Systems of Equations, Programming Matrix Operations
Introduction to Computational Calculus
Computational MethodsDifferentiationDifferential EquationsBasic Integration
Numerical Methods for Calculus
Approximations MethodsComputational Solutions
Gradients
Partial Differentiation and GradientsGradients of MatricesUseful Identities for Computing Gradients
Optimisation
Algorithmic Approaches to Continuous OptimisationOptimisation using Gradient Descent
The emphasis of this course will be on computational approaches to explore these topics, since this is how these concepts manifest themselves in Machine Learning. Students will create, analyze and implement algorithms for computing matrix operations, solving systems of equations, basic calculus especially approximation algorithms. Since gradient descent is a key concept algorithmic approaches to this will be considered.
| Module Content & Assessment | |
|---|---|
| Assessment Breakdown | % |
| Formal Examination | 50 |
| Other Assessment(s) | 50 |