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課程名稱 |
人工智慧概論
Introduction to Artificial Intelligence |
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開課學期 |
112-1 |
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授課對象 |
生物資源暨農學院 生物機電工程學系 |
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授課教師 |
陳倩瑜 |
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課號 |
BME3114 |
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課程識別碼 |
611 39000 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
必帶 |
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上課時間 |
星期四7,8,9(14:20~17:20) |
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上課地點 |
知武會議室 |
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備註 |
本校人工智慧領域專長課程
總人數上限：70人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
• Learn to understand and apply the fundamental theories of artificial intelligence.
• Discuss the historical development, current status, and future trends of artificial intelligence.
• Learn and understand core AI technologies such as machine learning and deep learning.
• Through hands-on homework assignments, learn how to use AI to solve practical problems. |
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課程目標 |
• Introduction to Artificial Intelligence: This includes the definition, historical development, basic concepts, and application areas of AI.
• Fundamentals of Machine Learning: This includes supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning.
• Fundamentals of Deep Learning: This includes neural networks, convolutional neural networks, recurrent neural networks, variational autoencoders, generative adversarial networks, etc.
• Practical Applications of AI: Such as computer vision, natural language processing, recommendation systems, game AI, etc.
• Future Challenges and Opportunities in AI: Discuss ethical, social, and legal issues associated with AI.
• AI Implementation: Learn to use AI tools and techniques to solve problems through hands-on homework assignments. |
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課程要求 |
Prerequisites:
Programming (ideally Python)
Probability and statistics
Linear algebra |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
1. Artificial Intelligence: A Modern Approach, 4th ed. by Stuart Russell and Peter Norvig, 2021 |
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評量方式
(僅供參考) |
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No. |
項目 |
百分比 |
說明 |
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1. |
Homework |
40% |
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2. |
Midterm |
30% |
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3. |
Final |
30% |
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針對學生困難提供學生調整方式 |
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上課形式 |
以錄影輔助, 提供學生彈性出席課程方式 |
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作業繳交方式 |
延長作業繳交期限 |
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考試形式 |
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其他 |
由師生雙方議定 |
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