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Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS MAT 4078 MATHEMATICS FOR MACHINE LEARNING II ELECTIVE 4 0 0 7

Offered By

Mathematics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR SELÇUK DEMIR

Offered to

Mathematics

Course Objective

The aim of this course is to learn advanced mathematical background which are necessary for machine learning.

Learning Outcomes of the Course Unit

1   Will be able to learn linear regression problems. 2   Will be able to have basic knowledge about maximum likelihood. 3   Will be able to learn the concept of principal component analysis. 4   Will be able to apply the methods for dimensional reduction. 5   Will be able to learn maximum variance and projection perspectives. 6   Will be able to make classification with support vector machines.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Linear regression problems. 2 Bayesian linear regression problems. 3 Maximum likelihood as orthogonal projection. 4 Dimensionality reduction with principal component analysis. 5 Maximum variance perspective, projection perspective. 6 Eigenvector computation and low-rank approximations. 7 Principal component analysis in high dimensions, key steps of principal component analysis. 8 Midterm. 9 Latent variable perspective. 10 Density estimation with Gaussian mixture models. 11 Parameter learning, the expectation maximization algorithm. 12 Support vector machines. 13 Separating hyperplanes. 14 Primal and dual support vector machines.

Recomended or Required Reading

Textbook(s): Deisenroth, M. P., Faisal, A. A., Ong, C. S., Mathematics for Machine Learning, Cambridge University Press, 2020. Supplementary Book(s): References: Materials: Instructor s notes and presentations.

Planned Learning Activities and Teaching Methods

Lecture Notes, Presentation, Problem Solving.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA 1 MTE MIDTERM EXAM 2 ASG ASSIGNMENT 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + ASG * 0.30 + FIN * 0.40 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + ASG * 0.30 + RST * 0.40 *** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Selçuk Demir selcuk.demir@deu.edu.tr Tel: 0232 3018581

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours) Lectures 13 4 52 Preparations before/after weekly lectures 12 4 48 Preparation for midterm exam 1 21 21 Preparation for final exam 1 30 30 Midterm 1 3 3 Final 1 3 3 Project Assignment 1 10 10 TOTAL WORKLOAD (hours) 167

Contribution of Learning Outcomes to Programme Outcomes

PO/LO PO.1 PO.2 PO.3 PO.4 PO.5 PO.6 PO.7 PO.8 PO.9 PO.10 PO.11 PO.12 PO.13 LO.1 5 5 5 4 5 5 4 LO.2 5 5 5 4 5 5 4 LO.3 5 5 5 4 5 5 4 LO.4 5 5 5 4 5 5 4 LO.5 5 5 5 4 5 5 4 LO.6 5 5 5 4 5 5 4