授課教師：謝啟民
本課程探討電腦、基因演算、大數據、人工智慧等運算科技介入於藝術表現，尤其之於審美觀點及美感經驗之影響，論述脈絡以Max Bense提出的資訊美學、負熵理論為出發，主題包括(1) Aesthetics Measure、Information Aesthetics，(2) Procedural Art，(3) Generative Art (4) AI Art。
課程分成兩大單元，第一單元講授程序性藝術、演算生成設計、AI風格模擬等，第二單元則進行實作，以高階著色語言GLSL為工具，透過coding做中學瞭解演算法之美。
| 週次 | 上課日期 | 課程進度、內容、主題 | 教師授課時數 |
|---|---|---|---|
| 1 | Introduction * 2/16 陽明交大校慶，全校停課一天 |
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| 2 | Aesthetics Measure, Information Aesthetics-1 | ||
| 3 | Aesthetics Measure, Information Aesthetics-2 | ||
| 4 | Aesthetics Measure, Information Aesthetics-3 | ||
| 5 | Hw1: Aesthetic Assessment | ||
| 6 | Procedural Art-1: noise, halftone, hatching | ||
| 7 | Procedural Art-2: noise, halftone, hatching | ||
| 8 | Procedural Art-3: noise, halftone, hatching | ||
| 9 | Hw2: GLSL Procedural art | ||
| 10 | AI Art-1：Style Transfer, DeepDream, Deep Style | ||
| 11 | AI Art-2：Style Transfer, DeepDream, Deep Style | ||
| 12 | Hw3: AI Art report | ||
| 13 | Generative Art-1: cellular automata, flocking | ||
| 14 | Generative Art-2: collective and collaborative | ||
| 15 | Generative Art-3: collaborative and evolutionary | ||
| 16 | Final project | ||
| 17 | Final project | ||
| 18 | Final Project demo |