Foundation of Machine Learning and Its Applications

Course Code:

COMP 311

Course Type:

Area Elective

P:

3

Lab:

0

Credits:

3

ECTS:

6

Course Language:

İngilizce

Course Objectives:

This course aims at providing a theoretical and practical basis for machine learning and its use with business problems.

Course Content:

Introduction to Machine Learning, Decision Trees, Instance Based Learning, Bayesian Learning, Logistic Regression, Neural Networks, Support Vector Machines, Model Selection, Feature Selection, Clustering, k-means, Expectation Maximization, Mixture of Gaussians, Ensemble Learning, Deep Learning, Adversarial Learning, Reinforcement Learning

Course Methodology:

1: Lecture, 2: Question-Answer, 3: Discussion, 4: Lab Work

Course Evaluation Methods:

A: Testing, B: Laboratory C: Homework D: Project

Vertical Tabs

Course Learning Outcomes

Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Methods
Student will understand machine learning fundamentals.	6	1,4	A,B,C
Student will learn a set of well-known supervised, unsupervised and semi-supervised learning algorithms.	6,9,8	1,2,3,4	A,B,C
Student will able to program solutions to some given real world machine learning problems.	6	1,2,3,4	A,B,C
Student will complete a project, write report and present in class on a topic in machine learning.	6	1	A
Given the parameters of a problem, students should be able to describe the advantages and disadvantages of different machine learning methods.	6	1,2,3,4	A,B,C

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction to Machine Learning	Lecture Notes
2	Decision Trees	Lecture Notes
3	Instance Based Learning	Lecture Notes
4	Bayesian Learning	Lecture Notes
5	Logistic Regression	Lecture Notes
6	Neural Networks	Lecture Notes
7	MIDTERM EXAMINATION
8	Support Vector Machines	Lecture Notes
9	Model Selection and Feature Selection	Lecture Notes
10	Clustering, k-means, Expectation Maximization, Mixture of Gaussians	Lecture Notes
11	Model Ensembles	Lecture Notes
12	Deep Learning	Lecture Notes
13	Adversarial Machine Learning	Lecture Notes
14	Reinforcement Learning
15	Final Examination

Recommended Sources

RECOMMENDED SOURCES
Textbook	Introduction to Machine Learning (2nd Edition), Ethem Alpaydin, The MIT Press,2010 Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer, 2006 Machine Learning, Tom Mitchell, McGraw-Hill, 1997
Additional Resources	Stephen Haunts , A Gentle Introduction to Agile Software Development, Stephen Haunts Ltd., 1th Ed., 2017.

Material Sharing

MATERIAL SHARING
Documents	Presentations and Laboratory Sheets furnished by MSAA
Assignments	Homework Sheets furnished by MSAA
Exams	Old exam questions are furnished

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Mid-terms	1	20
Projects	1	50
Assignment and Labwork	5	30
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Including the exam week: 16x Total course hours)	14	3	42
Hours for off-the-classroom study (Pre-study, practice)	14	3	42
Mid-terms	1	2	2
Project	1	30	30
Homework	5	6	30
Final examination	2 (Including reparation)	3	6
Total Work Load			152
Total Work Load / 25 (h)			6,08
ECTS Credit of the Course			6

None

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