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 |