COMPUTER ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
CMP4507	Text Mining	Spring	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Prof. Dr. ÇAĞATAY ÇATAL
Course Objectives:	Textual data is increasing in many environments such as articles, blogs, tweets, news, publications, and books. To work with such data and to discover knowledge from this huge amount of data; several techniques such as linguistics, machine learning, deep learning, and natural language processing are required and as such, this is indeed a very difficult task. The aim of this course is to provide the application of machine learning in textual documents in order to analyze the textual data quantitatively. Cleaning textual data, representation of this data, and generating textual data in different problems are three important issues that should be known while working with textual data in general.

Learning Outcomes

The students who have succeeded in this course;
1) Describe the methods for the preparation of textual data
2) Understand the basic methods of data representation
3) Apply the required techniques for text classification problems
4) Understand the methods for language modeling
5) Understand how to create textual description from the picture.
6) Describe the methods required for machine translation from one language to another.

Course Content

1) "
Introduction to Text Mining and related topics (natural language processing, machine learning, deep learning, opportunities offered by deep learning)"
2) Explanation of data preparation methods (manual text cleaning, cleaning with NLTK, data preparation with scikit-learn, data preparation with Keras)
3) Explanation of data representation models (Bag-of-words model, preparation of movie review data for sentiment classification problem)
4) Word embeddings used in data representation
5) Explanation of the methods that can be applied in the text classification problem
6) Midterm
7) Explanation of character and word based language models
8) Explanation of the methods that can create textual definition from the picture (Image captioning)
9) Machine translation from one language to another
10) Practical Application: Sentiment analysis with artificial neural network based bag-of-words model
11) Practical Application: Sentiment analysis with word embedding and CNN model, Sentiment analysis with N-gram based CNN model
12) Practical Application: Designing a Neural Network based language model for Text Generation
13) Practical Application: Designing artificial neural network based image captioning model
14) Practical Application: Designing artificial neural network based machine translation model

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to Text Mining and related topics (natural language processing, machine learning, deep learning, opportunities offered by deep learning)
2)	Explanation of data preparation methods (manual text cleaning, cleaning with NLTK, data preparation with scikit-learn, data preparation with Keras)
3)	Explanation of data representation models (Bag-of-words model, preparation of movie review data for sentiment classification problem)
4)	Word embeddings used in data representation
5)	Explanation of the methods that can be applied in the text classification problem
6)	Explanation of character and word based language models
7)	Explanation of character and word based language models
8)	Review for the midterm exam
9)	Explanation of the methods that can create textual definition from the picture (Image captioning)
10)	Explanation of the methods that can create textual definition from the picture (Image captioning)
11)	Machine translation from one language to another
12)	Machine translation from one language to another
13)	Practical applications
14)	Practical Applications

Sources

Course Notes / Textbooks:	Brownlee, J. (2017). Deep Learning for Natural Language Processing: Develop Deep Learning Models for your Natural Language Problems. Machine Learning Mastery.
References:	Ignatow, G., & Mihalcea, R. (2017). An introduction to text mining: Research design, data collection, and analysis. Sage Publications.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance	42	% 0
Project	57	% 40
Midterms	15	% 20
Final	20	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 20
PERCENTAGE OF FINAL WORK		% 80
Total		% 100

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and computer engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2)	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3)	Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4)	Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; ability to use information technologies effectively.
5)	Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or computer engineering research topics.
6)	Ability to work effectively within and multi-disciplinary teams; individual study skills.
7)	Ability to communicate effectively in verbal and written Turkish; knowledge of at least one foreign language; ability to write active reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9)	To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10)	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11)	Knowledge of the effects of engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in engineering; awareness of the legal consequences of engineering solutions.