COURSE UNIT TITLE

: MATHEMATICS FOR MACHINE LEARNING II

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ELECTIVE

Offered By

Mathematics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

DOCTOR CEM ÇELIK

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.
8 Key steps of principal component analysis.
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


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

English

Course Policies and Rules

You can be successful in this course by studying from your textbooks and lecture notes on the topics to be covered every week, coming to class by solving the given problems, establishing the concepts by discussing the parts you do not understand with your questions, learning the methods, and actively participating in the course.

Contact Details for the Lecturer(s)

Cem Çelik
cem.celik@deu.edu.tr
Phone: 0(232) 301 85 80

Office Hours

To be announced later.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 4 56
Preparations before/after weekly lectures 14 4 56
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) 179

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.15554554
LO.25554554
LO.35554554
LO.45554554
LO.55554554
LO.65554554

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

Copyright 2015 DEÜ. BİD

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