Computer Process System Engineering: Courses

機械學習與數位決策於化工過程 (Machine Learning and Digital Decision Making in Chemical Processes)
- The world is undergoing rapid changes, and industrial AI is essential to coping with the challenges and opportunities of the new era. (世界正發生劇變，產業AI化勢在必行) Data analytics and machine learning have become one of the most studied subjects in process systems engineering, attracting significant research and application interests. Particularly, large companies in Taiwan (TSMC, FPC, and USIG, etc.) and in the world (Google, Facebook, Apple, Amazon, IBM, etc.) heavily invest in machine learning research and applications for good reasons. Machine learning and digital decision making also constitute the essential knowledge that process system and control engineers should be equipped with.
- The goal of this course is:
  - To provide students with some advanced machine learning schemes and process data analytics, and
  - To help students understand problem-solving methods and digital decision making in complex chemical processes.
- The course requires basic knowledge in principles of chemical engineering, numerical methods, probability, statistics, calculus, optimization and some computer programming experience, although it certainly is advantageous to have prior exposure--not strictly required.
- (Spring 2026)
  - Class Meeting Times: 13:00-14:30 (TUE) and 13:00-14:30 (WED) (CYCU)
  - Class Meeting Times: 14:00-16:50 (THU) (NCU)
- Course Structure.
  - Introduction
    - (2-19-2026)
      - General Lecture: AI in Chemical Engineering
    - (2-26-2026)
      - Course Overview
  - Python & Model Simulation
    - (2-26-2026)
      - Intensive Python Training
    - (3-05-2026)
      - Model Simulation (Nonlinear Systems & Dynamic Nonlinear Systems)
  - Machine Learning & Deep Learning for Classification and Regression
    - (3-05-2026; 3-12-2026)
      - The Beginning — Getting to Know Machine Learning
    - (3-19-2026; 3-24-2026)
      - Classic Machine Learning Algorithms
    - (3-24-2026; 4-09-2026; 4-16-2026)
      - Neural Networks, Convolution Neural Networks
  - Generative Models
    - (4-16-2026)
      - What is a Generative model
      - Generative model families
    - (4-16-2026)
      - Autoencoders & Variational Autoencoders
    - (4-23-2026)
      - Generative Adversarial Networks
  - Sequential Data Modeling
    - (5-07-2026)
      - Window Data Modeling & RNN
    - (5-14-2026)
      - LSTM
    - (5-21-2026)
      - Attention and Transformer
  - Decision Making (Reinforcement Learning)
    - (5-28-2026)
      - Reinforcement Learning
      - Actor-Critic Learning
    - (6-4-2026)
      - Q learning
    - (6-11-2026)
      - DQN
  - Project Presentation (6-24-2026)
- Homework: Python would be used for the programming portions of the assignments. During the first week, a tutorial session would be hosted to jump-start your transition into working in Python
  - (03-12-2026) Assignment#0: Selection of Dynamic Nonlinear System Modeling for Data Generation in AI Applications
  - (04-27-2026; 05-04-2026) Assignment#1: Classification
  - (04-27-2026; 05-04-2026) Assignment#2: (Static) Regression
  - (06-03-2026) Assignment#3: Nonlinear Process Modeling
  - (06-23-2026) Assignment#4: Nonlinear (Dynamic) Process Design
  - Pop Quiz 1
  - Pop Quiz 2
  - Pop Quiz 3
- Grading Distribution
  - No quiz and exam will be given. Several homework will be assigned. Homework will be assigned periodically. The homework will consist of applying one of the techniques design methods presented in the course to a problem chosen by students. Analysis and simulation will be expected. Hope that these applications can inspire your fantasy, which contributes to new applications of these techniques in chemical problems or other fields
  - Homework (four to five times): 65%
  - Project: 20%
  - Class activity (Q&A and presentation): 15%
  - Note that late assignments will not be allowed unless a legitimate reason (illness, religious convention, etc) exists and is discussed with the instructor.
- Grades (Submission Records)