COURSE UNIT TITLE

: MACHINE LEARNING TECHNIQUES I

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
EMT 3021 MACHINE LEARNING TECHNIQUES I COMPULSORY 3 0 0 5

Offered By

Econometrics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR SERKAN ARAS

Offered to

Econometrics (Evening)
Econometrics

Course Objective

The main objective of the course is to introduce basic concepts in machine learning techniques, to identify the near-optimal hyperparameters effectively and efficiently, and to gain the abilities required for applying machine learning with a programming language.

Learning Outcomes of the Course Unit

1   To be able to define machine learning.
2   To be able to describe data pre-processing techniques.
3   To be able to model decision trees problems.
4   To be able to find the near-optimal hyperparameters.
5   To be able to utilize a programming language for 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 What is machine learning Why use machine learning Examples from real life applications.
2 Types of machine learning systems. Main challenges of machine learning.
3 Supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
4 Overfitting the training data. Underfitting the training data.
5 Techniques for avoiding the overfitting problem and methods in data division.
6 Defining parameters and hyperparameters in a machine learning problem. Hyperparameter tuning and model selection.
7 Grid search, randomized search, and informed search methods.
8 Data preprocessing for machine learning algorithms: missing values, coding for categorical and text attributes, feature scaling, generating transformation pipelines.
9 Mid-term
10 Mid-term
11 Performing data preprocessing and data division techniques in real data on the python platform.
12 Performance measures commonly used in classification and regression problems.
13 Defining decision trees, and its important parameters. Training and visualising a decision tree.
14 Implementing a decision trees algorithm in the python platform to real-life problems.

Recomended or Required Reading

Suggested Sources for the Course:
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

Class work and hands on exercises at lab.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 MTEG MIDTERM GRADE MTEG * 1
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE MTEG * 0.40 + FIN * 0.60
5 RST RESIT
6 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

It is 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 3 36
Preparation for midterm exam 1 15 15
Preparation for final exam 1 15 15
Preparation for quiz etc. 1 10 10
Final 1 1 1
Midterm 1 1 1
Quiz etc. 1 1 1
TOTAL WORKLOAD (hours) 115

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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