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◈ 學分學程介紹 Program Overview
為配合協助政府的工業相關產業創新計畫，驅動產業發展量能，為因應這波人工智慧革命，本學分學程屬於人工智慧應用學分學程，主要給跨領域學生修習，因此課程設計從先修的統計開始，建議學生在修課之前仍需修習程式設計課程，接下來透過循序漸進的修課規劃，才能在工業上創新。
To support government-led industrial innovation initiatives and boost industrial development capacity in response to the ongoing AI revolution, this program—under the Industrial AI Applications Program—is primarily designed for cross-disciplinary students.
The curriculum begins with statistics as a prerequisite, and it is recommended that students also take programming courses beforehand. A step-by-step learning progression is provided to equip students with the skills necessary for industrial innovation through AI.
◈ 適合學生 Who should take this program
本學分學程適合理工學院的學生，或是已經完成「人工智慧探索應用學分學程」的學生。
This program is suitable for students from colleges of science and engineering, or for students who have already completed the Applied AI Exploration Program.
◈ 修課規定 Program Requirements
- 本聯盟各學分學程總修習學分為 15 學分。
- 本聯盟各學分學程間可互相抵免學分上限為 6 學分。
- 若需取得本聯盟頒發學程學分證明，學生必須在各該學分學程中修習至少 8 學分以上聯盟認定課程，包括主導課程(鏡像課程)與衛星課。
- Each TAICA program requires a total of 15 credits.
- Up to 6 credits may be cross-counted between different TAICA programs.
- To receive a TAICA-issued certificate, students must complete at least 8 credits of TAICA-recognized courses, which includes both master (mirror) courses) and satellite courses.
◈ 修課注意事項
學生修習課程的時候，若因為主修課程安排限制，不一定要根據課程規劃中的修課順序建議，舉例來說：在本學分學程若跳過統計來修機器學習，也是可行，但是可能對課程理解、和課程表現上會較為遜色。又，雖然人工智慧倫理的課程難度是最簡單的，但是若沒有按照修課建議順序，有可能會在少部分課程內容上會有囫圇吞棗之憾。因此若選課上有疑惑，請和開課老師討論、或在學期初提前理解課程內容進度，再審慎規劃。
Students are not strictly required to follow the recommended course sequence, especially if constrained by their major’s scheduling. For example, it is possible to take Machine Learning without first taking Statistics, but this may lead to reduced comprehension and performance. Similarly, while AI Ethics is one of the easiest courses, taking it out of sequence may result in gaps in understanding some content.
If you have questions about course selection and planning, please consult the course instructor, or review course content early in the semester to make a well-informed decision.