This course introduces the fundamental theories of numerical analysis, data analysis, and artificial intelligence, which are actively used in various fields of chemical engineering. It also aims to develop the ability to directly program related software using Python and to understand how these techniques are applied in major areas of chemical engineering.

The course is structured into four main parts:
(1) Introduction to Python: A basic introduction to the Python language and an overview of Python libraries widely used in mathematics, science, and engineering.
(2) Numerical Computing: An introduction to numerical analysis methods commonly used in chemical engineering and AI, with hands-on programming of numerical tools using Python.
(3) AI Algorithm Basics: A presentation of fundamental theories related to artificial intelligence, along with simple AI model programming using PyTorch.
(4) Applications of AI in Chemical Engineering: A review of specific examples of how artificial intelligence is being applied in the field of chemical engineering.

Not mandatory, but recommended to take CSED105 and CSED101

Attendance: 20% (수업일수 1/4 이상 결석시 자동으로 F)
Assignments: 40%
Final exam: 40%

“Deep learning”, Ian Goodfellow
“Computational Physics”, Mark Newman
"Mathmatics for machine learning", Marc Peter Deisenroth
"Introduction to linear algebra", Gilbert Strang

Part 1. Introduction to Python
- Programming basics I
- Programming basics II

Part 2. Computer programming for science and engineering
- Computational analysis and visualization methods
- Numerical computing
- Linear Algebra
- Optimization and Linear Regression

Part 3: AI algorithm basics
- Classification and Clustering
- Activation function
- Back propagation
- Deep learning and CNN / Attention & Transformer
- Programming Deep-NN with PyTorch

Part 4: Applications of AI
- Applications in material science
- Applications in bio engineering
- Applications in process systems engineering

- 대면강의 기본
- 상황에 따라 비대면 및 녹화강의로 대체

없음