COURSE UNIT TITLE

: MACHINE LEARNING TECHNIQUES II

DEGREE PROGRAMMES

Description of Individual Course Units

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

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR RABIA ECE OMAY

Offered to

Econometrics (Evening)
Econometrics

Course Objective

The main objective of the course is to present the most used machine learning algorithms to students, to provide them the understanding of which one is the most appropriate for the problems they encounter in real-life and to apply them effectively by using a programming language.

Learning Outcomes of the Course Unit

1   To be able to define basic machine learning algorithms.
2   To be able to introduce simple ensemble methods.
3   To be able to produce good predictions by machine learning algorithms.
4   To be able to find solutions for the real-life problems.
5   To be able to apply a programming language for the commonly encountered machine learning problems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Naïve Bayes algorithm and its application fields.
2 Implementing the Naïve Bayes algorithm using real-life data on the programming environment.
3 Introducing the random forests algorithm, and its significant hyperparameters and applications fields.
4 Performing the random forests algorithm with real data using the programming environment.
5 Logistic regression and finding its hyperparameters with data search algorithms on the programming environment.
6 Defining Support Vector Machines (SVM). Dealing with the problems of classifications and regression with the help of the SVM.
7 Applying SVM models with real data on the programming environment.
8 Simple ensemble methods and applying them to real data.
9 Mid-term
10 Mid-term
11 Introducing Bagging (bootstrap aggregating) and Boosting algorithms.
12 Unsupervised learning techniques: k-means algorithms and its applications.
13 DBSCAN algorithm and applying it on the programming environment.
14 Other unsupervised learning techniques

Recomended or Required Reading

1. Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. " O'Reilly Media, Inc.".
2. Raschka, S., & Mirjalili, V. (2019). Python machine learning: Machine learning and deep learning with Python, scikit-learn, and TensorFlow 2. Packt Publishing Ltd.
3. Burkov, A. (2019). The hundred-page machine learning book (Vol. 1, p. 32). Quebec City, QC, Canada: Andriy Burkov.

Planned Learning Activities and Teaching Methods

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 QUZ QUIZ
2 MTE MIDTERM EXAM
3 MTEG MIDTERM GRADE QUZ * 0.25 + MTE * 0.75
4 FIN FINAL EXAM
5 FCG FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60
6 RST RESIT
7 FCGR FINAL COURSE GRADE (RESIT) MTEG * 0.40 + RST * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Doç.Dr. Serkan ARAS: serkan.aras@deu.edu.tr
Prof.Dr.Ipek DEVECI KOCAKOÇ: ipek.deveci@deu.edu.tr

Office Hours

Will be determined the status of course schedule for each term.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 12 3 36
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 20 20
Preparation for final exam 1 25 25
Preparation for quiz etc. 1 20 20
Midterm 1 1 1
Final 1 1 1
Quiz etc. 1 1 1
TOTAL WORKLOAD (hours) 116

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.11
LO.21
LO.31
LO.41
LO.51

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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