Bachelor
2024/2025
Development and Analysis of Requirements
Type:
Compulsory course (Artificial and Augmented Intelligence Technologies)
Area of studies:
Software Engineering
Delivered by:
Joint Department with MERA Group
When:
4 year, 3 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Natalia Aseeva
Language:
English
ECTS credits:
4
Course Syllabus
Abstract
The program is intended for teachers who teach this discipline, teaching assistants and students of the training direction 09.03.04 "Software Engineering", studying the discipline "Software Design". The program is taught in English. The program was developed in accordance with: OS FGAOU VPO NRU HSE in the direction 09.03.04 "Software Engineering"; The curriculum of the university in the direction 09.03.04 "Software engineering".
Learning Objectives
- Acquisition of knowledge and practical experience in software requirements development and analysis
- Practical mastery of modern methods of requirements elicitation and documentation
- Acquisition of skills of research work involving independent study of methods and tools for development and analysis of requirements for software projects.
Expected Learning Outcomes
- Name the key elements of the software architecture of modern information systems. Determine the projection of IP on the architecture of the enterprise. Formulate technologies and integration of heterogeneous CIS components
- Apply basic approaches to word embeddings, such as Count-based methods, Word2Vec, Glove
- Apply classic machine learning methods such as Naive Bayes, SVM, LR and deep learning approaches such as FCN, CNN, LSTM for text classification problem
- Applying open-source libraries for text preprocessing, such as Natasha and nltk. Resume the following common problems: Expand Contractions, Lower Case, Remove Punctuations, Remove words and digits containing digits, Remove Stopwords, Rephrase Text, Stemming and Lemmatization, Remove White spaces
- Apply various text-generation techniques such as N-grams LMs and Neural LMs
- Applying the mechanisms of attenuations and transformers to seq2seq problems
- Apply special data preprocessing techniques and architectures like Bert to the NER problem
- Apply modern architecture Bert
- Apply of the Burt architecture and its modifications to the problem QA
- Apply NDA, NMF and LSA to Topic modeling problem
Course Contents
- Key elements of the software architecture of modern information systems, the projection of IS on the architecture of the enterprise. Overview of integration technologies for heterogeneous CIS components.
- Word embedding
- Text classification
- Text preprocessing methods
- Language Modeling
- Seq2seq models
- Named Entity Recognition
- Domain Adaptation
- Transfer learning
- Question Answering
- Topic Modeling
Bibliography
Recommended Core Bibliography
- Introduction to natural language processing, Eisenstein, J., 2019
- Yu, C., Wang, J., Chen, Y., & Huang, M. (2019). Transfer Learning with Dynamic Adversarial Adaptation Network. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsarx&AN=edsarx.1909.08184
Recommended Additional Bibliography
- Aman Kedia, & Mayank Rasu. (2020). Hands-On Python Natural Language Processing : Explore Tools and Techniques to Analyze and Process Text with a View to Building Real-world NLP Applications. Packt Publishing.