Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN442Natural Language Processing Laboratory0+2+01412.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To form a basis for Natural Language Processing in order to prepare the student for advanced Natural Language Processing that will be taught as a graduate course.
Course Content Introduction to natural language processing
Morphological analysis
Part-of-speech tagging
Parsing algorithms
Semantic analysis
Natural language processing applications
Introduction to Deep Learning
Course Methods and Techniques Discussion, Drill and Practice, Preparing & Presenting Reports
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 ) and ( AIN440 )
Course Coordinator None
Name of Lecturers Prof. Ebru Akçapınar Sezer
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Daniel Jurafsky, and James H. Martin, "Speech and Language Processing",Prentice Hall, 2000. James Allen, "Natural Language Understanding", Second edition, The Benjamin/Cumings Publishing Company Inc., 1995 Christopher D. Manning, and Hinrich Schutze, "Foundations of Statistical Natural Language Processing", The MIT Press, 1999.
Course Notes Daniel Jurafsky, and James H. Martin, "Speech and Language Processing", Prentice Hall, 2000.

James Allen, "Natural Language Understanding", Second edition, The Benjamin/Cumings Publishing Company Inc., 1995.

Christopher D. Manning, and Hinrich Schutze, "Foundations of Statistical Natural Language Processing", The MIT Press, 1999.


Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Assignment 4 % 85
Laboratory Work 6 % 15
Total
10
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Hours for off-the-c.r.stud 14 3 42
Assignments 4 12 48
Laboratory 14 2 28
Total Work Load   Number of ECTS Credits 3,93333333333333 118

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 The student will gain general knowledge about natural language processing subfields, the student will have a general understanding about part-of-speech-tagging, morphological analysis, parsing algorithms, semantic analysis, the student will have a basis f
2  
3  
4  
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to Natural Language Processing
2 Language Models and N-grams
3 Regular Expressions and Finite State Automata
4 Hidden Markov Models and Part-of-Speech Tagging
5 Midterm
6 Context Free Grammars and Parsing Algorithms
7 Semantics (Lexical, compositional semantics, word sense disambiguation)
8 Introduction to Deep Learning
9 Word embeddings
10 Computation Graphs
11 Midterm
12 Recurrent Neural Networks and Language ModellingI
13 Encoder-decoder models and Machine Traslation
14 Other Application Fields

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 3 4 4 2 3 1 2 1 2 5 5 3
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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