2025/2026
Neural Networks and Deep Learning
Type:
Mago-Lego
Delivered by:
Department of Business Informatics
Where:
Graduate School of Business
When:
1, 2 module
Open to:
students of one campus
Instructors:
Seungmin Jin
Language:
English
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
- Students know advanced methods for training and regularizing neural networks.
- 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
- Owns the concepts of: logistic regression; gradient descent; neural networks and gradient backpropagation 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
- Able to use embedding for tabular data and recommenders
- Able to use momentum and advanced optimizers for stochastic gradient descent
- Able to use residual blocks with neural networks
- Can construct a digit classifier using a deep learning model
- Can construct a neural network from scratch
- Can construct recurrent neural network from scratch
- Can solve multi-class and multi-label problems with deep learning
- Knows advanced neural networks such as U-Net and Siamese
- Knows and uses state-of-the-art approaches to train neural networks
- Knows the definitions of deep learning
- Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
- Understands approaches to put machine learning system into production
- Understands approaches to solve natural language processing problems
- Understands data ethics and able to detect ethical problems
- Understands the role of convolutions in image processing
- • Train MLP for supervised learning taks
- Can apply generative models for solving complex tasks.
- Explain the mechanics of basic building blocks for neural networks.
- Define and train a CNN from scratch.
- Understand modern architectures of RNNs: LSTM, GRU.
- Understand the transfer learning ideas and advantages
- • understand the operations of a self-attention layer in Transformers
- Able to train autoencoders
- Владеет навыками взаимодействия с языковыми моделями, в частности, с ChatGPT, включая структурирование запросов, верификацию информации и использование различных шаблонов для оптимизации взаимодействия.
- Utilize AI Tools: Demonstrate proficiency in using no-code/low-code AI tools like KNIME and ChatGPT to develop and implement AI solutions, reducing the reliance on extensive coding skills.
- A student should formulate optimization problems as network models.
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.
