• HOME
• ABOUT US
  • Name Address
  • General Description
  • Governance
  • Academic Calendar
  • Degree Programmes
  • List of Programmes in English
  • General Admission and Registration Procedures
  • Credit Mobility and Recognition of Prior Learning
  • ECTS Credit Allocation
  • Academic Guidance
  • Contacts
• Degree Programmes
  • Third Cycle Programmes (Doctorate Degree)
  • Second Cycle Programmes (Master's Degree)
  • First Cycle Programmes (Bachelor's Degree)
  • Short Cycle Programmes (Associate's Degree)
• GENERAL INFORMATION FOR STUDENTS
  • Cost of Living
  • Accommodation
  • Meals
  • Medical Facilities
  • Facilities for Disabled Students
  • Insurance
  • Financial Supports
  • Student Affairs Office
  • Practical Information for International Students
  • Language Courses
  • Work Placement Possibilities
  • Sports, Social and Cultural Facilities
  • Student Associations and Clubs
  • About İzmir - Turkey
• INTERNATIONAL RELATIONS
  • International Relations Office
  • Registration Procedures
  • Agreements
  • Coordinators
  • Documents
  • List of Programmes in English
  • ECTS / DS Labels

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS MAT 4077 MATHEMATICS FOR MACHINE LEARNING I ELECTIVE 4 0 0 7

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 basic mathematical background which are necessary for machine learning.

Learning Outcomes of the Course Unit

1   Will be able to learn the basic concepts of linear equation systems and matrices which are necessary for machine learning. 2   Will be able to use concepts such as vector spaces, norms, inner products, orthogonal projections, matrices, matrix decomposition and matrix approach which are necessary for machine learning. 3   Will be able to calculate partial derivatives, higher order derivatives, gradients and multivariable Taylor series. 4   Will be able to learn basic optimization methods used in machine learning. 5   Will be able to have knowledge about data, models and basics of learning. 6   Will be able to produce basic machine learning models.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description 1 Systems of linear equations, matrices, solving systems of linear equations. 2 Vector spaces, linear independence, linear mappings, affine spaces. 3 Norms, inner products, orthogonal basis, inner product of functions, orthogonal projections, rotations. 4 Determinants and traces, eigenvalues and eigenvectors, matrix decompositions, matrix approximations. 5 Partial differentiation and gradients, gradients of matrices, higher-order derivatives. 6 Linearization and multivariate Taylor series. 7 Introduction to machine learning and basic concepts. 8 Data and models 9 Learning and model training. 10 Least squares method. 11 Gradient descent optimization. 12 Introduction to deep learning and basic concepts. 13 Classification problems with deep learning. 14 End-to-end model building with deep learning.

Recomended or Required Reading

Textbook(s): Deisenroth, M. P., Faisal, A. A., Ong, C. S., Mathematics for Machine Learning, Cambridge University Press, 2020.

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 QUZ QUIZ 3 FIN FINAL EXAM 4 FCG FINAL COURSE GRADE MTE * 0.30 + QUZ * 0.20 + FIN * 0.50 5 RST RESIT 6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.30 + QUZ * 0.20 + RST * 0.50 *** 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

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 24 24 Preparation for final exam 1 24 24 Preparation for quiz etc. 1 8 8 Midterm 1 3 3 Final 1 3 3 Quiz etc. 1 1 1 TOTAL WORKLOAD (hours) 175

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