2025/2026
Neural Networks and Deep Learning
Type:
Mago-Lego
Delivered by:
Department of Business Informatics
When:
3, 4 module
Open to:
students of one campus
Instructors:
Seungmin Jin
Language:
English
ECTS credits:
6
Contact hours:
48
Course Syllabus
Abstract
This course introduces Master's students in Business Informatics—especially those with non-technical backgrounds—to neural networks and deep learning through an intuitive, low-code lens. Emphasizing practical training over coding expertise, it leverages ChatGPT for prompt engineering, code generation, and natural-language explanations to demystify algorithms like MLPs, CNNs, RNNs, and transformers. Students will explore forward/backpropagation, model architectures, and applications in business scenarios such as forecasting, image analysis, and anomaly detection.
Outcomes include: intuitive grasp of DL processes, low-code model training for business problems, ethical roadmaps, and a final prototype project. Ultimately, the course transforms deep learning from a technical hurdle into a strategic business tool, fostering analytics-driven innovation in resource-constrained environments.
cf. This course evaluates students using the normalized scores.
Learning Objectives
- Learn to effectively apply deep learning techniques to real-world business problems in computer vision, natural language processing, and tabular data
- Acknowledge ethical implications of applying machine learning in practice
Expected Learning Outcomes
- Владеет механизмами: multi-head attention, self-attention, маскирование, сглаживание в механизме внимания, positional encoding. Понимает ахитектуру transformer.
- Knowing how to evaluate the quality of a neural network model
- Understanding the neural network learning algorithm
- Knows optimization algorithms for deep neural networks based on various variations of gradient descent. Configures such algorithms based on the conditions of a specific task
- Владеет навыками взаимодействия с языковыми моделями, в частности, с ChatGPT, включая структурирование запросов, верификацию информации и использование различных шаблонов для оптимизации взаимодействия.
Course Contents
- Introduction to Neural Networks
- Low-Code Deep Learning with ChatGPT
- Forward Propagation and Backpropagation
- Multilayer Perceptrons and Training Basics
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) and LSTMs
- Autoencoders
- Generative Models and Transfer Learning
- Transformer and Attention
- Ethics, Bias, and Optimization
- Capstone Project
Assessment Elements
- AttendanceStudents who miss more than 25% of the classes and also fail to complete the presentation assignments may fail this course.
- ExamExam Question (Select All That Apply)
- Capstone ProjectStudents will demonstrate practical skills; produce deployable DL solutions with business impact.
- Capstone ProjectThis part evaluates if their model is working properly and reproducible.
Interim Assessment
- 2025/2026 4th moduleThe final grade is calculated by weighting and summing the raw scores, followed by min-max normalization, with caps on grade distributions based on student ranks to adjust the overall distribution. The specific formulas and distribution details are as follows. Raw Score Calculation Formula The overall raw score is computed as a weighted sum: Raw Score = 0.2 × Attendance + 0.25 × Exam + 0.25 × Capstone Project + 0.3 × Presentation Min-Max Normalization Formula The raw score is normalized using the minimum (min) and maximum (max) values across all students: Normalized Score = (Raw Score - min(All Raw Scores)) / (max(All Raw Scores) - min(All Raw Scores)) × 100 Grade Distribution and Caps Normalized scores are assigned based on student rank order, with caps applied to maintain the following distribution across all students: 5 points (top tier): Up to 30% of students 4 points: Up to 40% of students 3 points or below: The remaining 30% of students