BL237 Artificial Intelligence Applications

3 ECTS - 2-1 Duration (T+A)- 3. Semester- 2 National Credit

Information

Code BL237
Name Artificial Intelligence Applications
Term 2024-2025 Academic Year
Semester 3. Semester
Duration (T+A) 2-1 (T-A) (17 Week)
ECTS 3 ECTS
National Credit 2 National Credit
Teaching Language Türkçe
Level Ön Lisans Dersi
Type Normal
Label C Compulsory
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Öğr. Gör. Mahir ATMIŞ
Course Instructor Öğr. Gör. Mahir ATMIŞ (A Group) (Ins. in Charge)


Course Goal / Objective

To test the machine learning methods used in our age on current data.

Course Content

"Introduction and definitions, classification/regression problem, supervised learning, inear regression, the smallest squares of error, logistics regression, perceptron, bias-variance, feature selection, artificial neural networks, decision trees, support vector machines, unsupervised learning"

Course Precondition

None

Resources

Lecture Notes Mahir Atmış

Notes

Uygulamalar ile Python ve Yapay Zeka, Emrah Aydemir


Course Learning Outcomes

Order Course Learning Outcomes
LO01 Explains the concept of Artificial Intelligence and algorithms.
LO02 Explains the concept of Artificial Neural Networks and deep networks.
LO03 Teachs the concepts of supervision and unsupervised learning.
LO04 Teaches the concepts of clustering.
LO05 Tests learning algorithms on ready data sets.
LO06 Understands the basic logic of learning algorithms.
LO07 Uses state of the art learning algorithms.
LO08 Develops their own learning algorithm.


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal Gains basic, current and applied knowledge about Computer Technologies. 4
PLO02 Bilgi - Kuramsal, Olgusal Gains knowledge about occupational health and safety, environmental awareness, and quality processes.
PLO03 Bilgi - Kuramsal, Olgusal Has knowledge of basic electronic components comprising computer hardware and their operations.
PLO04 Bilgi - Kuramsal, Olgusal Has knowledge about Atatürk's Principles and History of Revolution.
PLO05 Beceriler - Bilişsel, Uygulamalı Keeps track of current developments and applications in computer programming, and utilizes them effectively. 4
PLO06 Beceriler - Bilişsel, Uygulamalı Has the ability to solve problems in the field of computer programming. 4
PLO07 Beceriler - Bilişsel, Uygulamalı Creates algorithms and data structures, and performs mathematical calculations. 5
PLO08 Beceriler - Bilişsel, Uygulamalı Explains and implements web programming technologies.
PLO09 Beceriler - Bilişsel, Uygulamalı Performs database design and management.
PLO10 Beceriler - Bilişsel, Uygulamalı Tests software and resolves errors.
PLO11 Beceriler - Bilişsel, Uygulamalı Can utilize software and package programs in the field of computer programming. 4
PLO12 Beceriler - Bilişsel, Uygulamalı Explains, designs and installs network systems.
PLO13 Beceriler - Bilişsel, Uygulamalı Uses word processor, spreadsheet, presentation programs.
PLO14 Yetkinlikler - İletişim ve Sosyal Yetkinlik Can effectively present thoughts on computer technologies through written and verbal communication, expressing them clearly and comprehensibly.
PLO15 Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği Takes responsibility as a team member to solve unforeseen complex problems encountered in practical applications of computer programming.
PLO16 Yetkinlikler - Öğrenme Yetkinliği Has awareness in career management and lifelong learning.
PLO17 Yetkinlikler - Alana Özgü Yetkinlik Has societal, scientific, cultural, and ethical values ​​in the collection, application, and announcement of results related to computer technologies.
PLO18 Yetkinlikler - İletişim ve Sosyal Yetkinlik Follows developments in the field using a foreign language and communicates with colleagues.
PLO19 Yetkinlikler - İletişim ve Sosyal Yetkinlik Can effectively communicate in Turkish both in written and oral forms.


Week Plan

Week Topic Preparation Methods
1 What's Learning? Preparation is not required. Öğretim Yöntemleri:
Anlatım
2 Clustering algorithms Preparation is not required. Öğretim Yöntemleri:
Anlatım
3 Classification algorithms Preparation is not required. Öğretim Yöntemleri:
Anlatım
4 Regression Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
5 Decision Trees Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
6 Support Vector Machines Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
7 Bayes Classification Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
8 Mid-Term Exam Ölçme Yöntemleri:
Yazılı Sınav
9 Artificial Neural Networks Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
10 Artificial Neural Networks (continuation) Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
11 Convolutional Neural Networks Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
12 Convolutional Neural Networks (continuation) Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
13 Reinforcement Learning Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
14 Unsupervised Learning Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
15 Unsupervised Learning (continuation) Preparation is not required. Öğretim Yöntemleri:
Alıştırma ve Uygulama
16 Term Exams Ölçme Yöntemleri:
Yazılı Sınav
17 Term Exams Ölçme Yöntemleri:
Yazılı Sınav


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 2 28
Assesment Related Works
Homeworks, Projects, Others 1 2 2
Mid-term Exams (Written, Oral, etc.) 1 5 5
Final Exam 1 10 10
Total Workload (Hour) 87
Total Workload / 25 (h) 3,48
ECTS 3 ECTS

Update Time: 14.05.2024 12:13