BAL609 - INTRODUCTION TO MACHINE LEARNING

Course Name Code Semester Theory
(hours/week)		 Application
(hours/week)		 Credit ECTS
INTRODUCTION TO MACHINE LEARNING BAL609 1st Semester 3 0 3 6
Prequisites
Course languageTurkish
Course typeMust 
Mode of DeliveryFace-to-Face 
Learning and teaching strategiesLecture
 
Instructor (s)Instructors of Department of Business Administration 
Course objectiveThe objective of this course is to establish a background on machine learning. 
Learning outcomes
  1. * knows basic terms about machine learning.
  2. * Knows supervised and unsupervised learning approaches.
  3. * Can solve various problems using Neural Network method.
  4. * Can code various machine learning techniques.
Course ContentBasic information on machine learning; supervised and unsupervised machine learning techniques; Neural Networks; coding studies on machine learning. 
ReferencesAlpaydın, E. (2014). Introduction to Machine Learning (Vol 2). The MIT Press
Mitchell, T. (1997) Machine Learning (Vol 1). McGraw-Hill Education 

Course outline weekly

WeeksTopics
Week 1What Is Machine Learning
Week 2Input-Output Functions
Week 3Logical Functions
Week 4Supervised and Unsupervised Learning
Week 5Statistical Learning
Week 6Computational Learning
Week 7Clustering Techniques
Week 8Midterm
Week 9Neural Networks - Threshold Logic Unit
Week 10Neural Networks - Linear Machines
Week 11Neural Networks - TLU Networks
Week 12Exemplary Coding Studies
Week 13Exemplary Coding Studies
Week 14Exemplary Coding Studies
Week 15Review
Week 16Final Exam

Assesment methods

Course activitiesNumberPercentage
Attendance
Laboratory
Application
Field activities
Specific practical training
Assignments
Presentation
Project
Seminar
Midterms
Final exam
Total
Percentage of semester activities contributing grade succes
Percentage of final exam contributing grade succes
Total

WORKLOAD AND ECTS CALCULATION

Activities Number Duration (hour) Total Work Load
Course Duration (x14) 0
Laboratory 0
Application0
Specific practical training0
Field activities0
Study Hours Out of Class (Preliminary work, reinforcement, ect)0
Presentation / Seminar Preparation0
Project0
Homework assignment0
Midterms (Study duration)0
Final Exam (Study duration) 0
Total Workload000

Matrix Of The Course Learning Outcomes Versus Program Outcomes

D.9. Key Learning OutcomesContrubition level*
12345
1. Conducts novel and ethical research on business, reports outcomes in a critical manner.    X
2. Solves problems via appropriate softwares, adapts to new methods and software.  X  
3. Has managerial and leadership skills to identify problems, objectives and strategic plans for organizational progress with a critical point of view.    X
4. Plays an active role in projects, analyses relationship between stakeholders accurately, motivates and manages all stakeholders through effective language skills. X   
5. Has necessary communication skills to manage verbal and written communication.   X 
6. Analyses and uses contemporary and advanced knowledge in relation with information from different areas.   X 
7. Progresses continuously and transfers the experience in both written and verbal ways.    X
8. Through anticipation and strategic thinking, plays an active role in organizational decision making process.  X  
9. Uses knowledge in consistency with the ethical, social and international values in an unbiased manner.   X 
10. Has expertise on the multi-disciplinary nature of management and related fields. X   
11. Approaches problems with a wide strategic perspective, self-develops continuously.  X  
12. Shares novel studies, is up to date both in knowledge and personal network. X   

*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest