This course will cover basics of NLP and applications of deep learning in natural language processing. Prerequisite for this class is Machine Learning.
Course Content
This course contains; Introduction,A simple NLP pipeline with scikit-learn,Word vectors,Recurrent Neural Networks,Language models,Pytorch and tensorflow,Text classification, text summarization, question answering,Exam Week study,Machine translation,Transformers,Lightweight AI,NLP systems in production,Project presentations,Project presentations.
Course Learning Outcomes
Teaching Methods
Assessment Methods
Implement advanced neural network architectures using tensorflow or pytorch.
2
E
Complete a full NLP project involving advanced concepts in machine learning
16, 2
D, F
Describe various NLP algorithms such as those used for text classification and text generation
12, 14, 21, 6, 9
A, D, G
Teaching Methods:
12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, G: Quiz
Course Outline
Order
Subjects
Preliminary Work
1
Introduction
2
A simple NLP pipeline with scikit-learn
3
Word vectors
4
Recurrent Neural Networks
5
Language models
6
Pytorch and tensorflow
7
Text classification, text summarization, question answering
8
Exam Week study
9
Machine translation
10
Transformers
11
Lightweight AI
12
NLP systems in production
13
Project presentations
14
Project presentations
Resources
Speech and Language Processing, Jurafsky and Martin, 3rd edition draft at https://web.stanford.edu/~jurafsky/slp3/
Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper at http://www.nltk.org/book/
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications.
X
2
Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas.
X
3
Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field.
X
4
Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field.
5
Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals.
X
6
Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements.
7
Independently perceive, design, apply, finalize and conduct a novel research process.
X
8
Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level.
X
9
Critical analysis, synthesis and evaluation of new and complex ideas in the field.
X
10
Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values.
X
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
50
Rate of Final Exam to Success
50
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
14
3
42
Guided Problem Solving
0
0
0
Resolution of Homework Problems and Submission as a Report
10
2
20
Term Project
0
0
0
Presentation of Project / Seminar
8
10
80
Quiz
6
3
18
Midterm Exam
1
30
30
General Exam
1
50
50
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
240
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(240/30)
8
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.
Detail Informations of the Course
Course Description
Course
Code
Semester
T+P (Hour)
Credit
ECTS
NATURAL LANGUAGE PROCESSING
COED1212914
Spring Semester
3+0
3
8
Course Program
Pazartesi 14:30-15:15
Pazartesi 15:30-16:15
Pazartesi 16:30-17:15
Prerequisites Courses
Recommended Elective Courses
Language of Course
English
Course Level
Third Cycle (Doctorate Degree)
Course Type
Elective
Course Coordinator
Prof.Dr. Selim AKYOKUŞ
Name of Lecturer(s)
Prof.Dr. Selim AKYOKUŞ
Assistant(s)
Aim
This course will cover basics of NLP and applications of deep learning in natural language processing. Prerequisite for this class is Machine Learning.
Course Content
This course contains; Introduction,A simple NLP pipeline with scikit-learn,Word vectors,Recurrent Neural Networks,Language models,Pytorch and tensorflow,Text classification, text summarization, question answering,Exam Week study,Machine translation,Transformers,Lightweight AI,NLP systems in production,Project presentations,Project presentations.
Course Learning Outcomes
Teaching Methods
Assessment Methods
Implement advanced neural network architectures using tensorflow or pytorch.
2
E
Complete a full NLP project involving advanced concepts in machine learning
16, 2
D, F
Describe various NLP algorithms such as those used for text classification and text generation
12, 14, 21, 6, 9
A, D, G
Teaching Methods:
12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 2: Project Based Learning Model, 21: Simulation Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, D: Oral Exam, E: Homework, F: Project Task, G: Quiz
Course Outline
Order
Subjects
Preliminary Work
1
Introduction
2
A simple NLP pipeline with scikit-learn
3
Word vectors
4
Recurrent Neural Networks
5
Language models
6
Pytorch and tensorflow
7
Text classification, text summarization, question answering
8
Exam Week study
9
Machine translation
10
Transformers
11
Lightweight AI
12
NLP systems in production
13
Project presentations
14
Project presentations
Resources
Speech and Language Processing, Jurafsky and Martin, 3rd edition draft at https://web.stanford.edu/~jurafsky/slp3/
Natural Language Processing with Python, Steven Bird, Ewan Klein, and Edward Loper at http://www.nltk.org/book/
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications.
X
2
Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas.
X
3
Evaluate and use new information within the field in a systematic approach and gain advanced level skills in the use of research methods in the field.
X
4
Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field.
5
Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals.
X
6
Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements.
7
Independently perceive, design, apply, finalize and conduct a novel research process.
X
8
Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level.
X
9
Critical analysis, synthesis and evaluation of new and complex ideas in the field.
X
10
Recognizes the scientific, technological, social or cultural improvements of the field and contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values.
