Program Overview

Program Overview

The university program in question is the "半導體AI與ChatGPT跨領域班" or the Semiconductor AI and ChatGPT Cross-Disciplinary Class, which is in its 17th iteration.

Course Details

Course Hours

The program consists of 108 hours of instruction.

Teaching Method

The course is taught through remote or distance learning.

Target Audience

The program is suitable for:

• Individuals without a programming background or industry knowledge who are interested in the field of AI.
• Those who wish to enter the AI field and are looking for a career change or transition.

Program Schedule

Course Period

The course runs from March 5, 2026, to July 18, 2026.

Registration Deadline

The deadline for registration is March 12, 2026, at 23:59.

Course Curriculum

The course covers the following topics:

• 半導體導論 (Semiconductor Introduction): Introduction to semiconductor concepts (3 hours).
• AI語言-python (AI Language - Python):
  • AI thinking and industrial applications.
  • Basic Python language (27 hours).
• 資料處理與視覺化 (Data Processing and Visualization):
  • Pandas data processing and Numpy numerical processing modules.
  • Matplotlib drawing module (9 hours).
• 機器學習 (Machine Learning):
  • Sklearn package, Kmeans, DBSCAN, and other clustering methods.
  • Regression, linear regression prediction, KNN, decision trees, random forests, SVM, and other commonly used machine learning classification methods.
  • Data splitting, model referencing, model training, and model prediction (6 hours).
• 深度學習 (Deep Learning):
  • Tensorflow and Keras packages.
  • Introduction to neurons, activation functions, forward processing, and backward correction learning.
  • DNN, CNN, RNN, LSTM model structures, uses, and parameter introductions.
  • Data splitting, model construction, model training, and model prediction for various models (21 hours).
• ChatGPT應用 (ChatGPT Application):
  • Using ChatGPT with image generation models to generate images and processing them with OpenCV.
  • Natural language processing techniques: word segmentation tools, cyclic neural networks, and self-encoding network introductions (18 hours).
• 案例研討與專案 (Case Discussion and Project):
  • Natural language - sentiment analysis.
  • Data processing - stock prediction.
  • Image processing - cancer prediction, semiconductor defect detection, and concentration detection (24 hours).

Total Hours

The total course hours are 108.

Additional Information

This is a self-paid course. For more details, please refer to the official course information or consult with the course instructor.