- Lecturer
- Murata Tsuyoshi
- Place
- Mon5-6(W833)
- Credits
- Lecture2 Exercise0 Experiment0
- Code
- 76017
- Syllabus updated
- 2008/10/1
- Lecture notes updated
- 2008/10/1
- Semester
- Fall Semester
Outline of lecture
荳弱∴繧峨ｌ縺溘ョ繝ｼ繧ｿ縺ｫ蜀蝨ｨ縺吶ｋ遏･隴倥ｄ豕募援繧偵さ繝ｳ繝斐Η繝ｼ繧ｿ縺ｫ繧医▲縺ｦ隕句ｺ縺呎ｩ滓｢ｰ蟄ｦ鄙偵ｮ謇区ｳ輔↓縺､縺縺ｦ隲悶§繧九ょ･蜉帙ョ繝ｼ繧ｿ縺ｫ髢｢縺吶ｋ蝓ｺ譛ｬ莠矩繧ｼ悟ｭｦ鄙偵↓繧医▲縺ｦ蠕励ｉ繧後ｋ讎ょｿｵ陦ｨ迴ｾ縺ｨ縺励※縺ｮ豎ｺ螳壽惠繧繝ｫ繝ｼ繝ｫ遲峨↓縺､縺縺ｦ逅隗｣繧呈ｷｱ繧√ｋ縺ｨ縺ｨ繧ゅ↓ｼ檎樟螳溽噪縺ｪ繝繝ｼ繧ｿ縺ｸ縺ｮ謇区ｳ輔ｮ驕ｩ逕ｨ縺ｫ縺､縺縺ｦ繧りｫ悶§繧九
Purpose of lecture
莠ｺ蟾･遏･閭ｽ縺ｫ縺翫¢繧句ｭｦ鄙呈焔豕輔↓縺､縺縺ｦ隲悶★繧九ょｭｦ鄙偵↓縺翫¢繧句･蜉帙ョ繝ｼ繧ｿ繧蠕励ｉ繧後ｋ遏･隴
縺ｮ陦ｨ迴ｾ縺ｨ縺励※縲∵ｱｺ螳壽惠繧繝ｫ繝ｼ繝ｫ縺ｪ縺ｩ縺ｮ蝓ｺ譛ｬ莠矩繧貞ｭｦ縺ｶ縺ｨ縺ｨ繧ゅ↓縲∫衍隴倡匱隕九ｄ讒矩
繧呈戟縺｣縺溘ョ繝ｼ繧ｿ縺九ｉ縺ｮ蟄ｦ鄙偵↑縺ｩ縺ｮ蠢懃畑縺ｫ縺､縺縺ｦ繧り蟇溘☆繧九
Plan of lecture
1. 讖滓｢ｰ蟄ｦ鄙偵→繝繝ｼ繧ｿ繝槭う繝九Φ繧ｰ, Weka
2. 讒矩縺ｮ險倩ｿｰ, 蟄ｦ鄙堤ｵ先棡縺ｮ蠢懃畑萓
3. 讎ょｿｵ遨ｺ髢, 繝舌う繧｢繧ｹ
4. 蜈･蜉帙ョ繝ｼ繧ｿ蠖｢蠑, 蛻鬘槫ｭｦ鄙, 逶ｸ髢｢蟄ｦ鄙
5. 蜈･蜉帙ョ繝ｼ繧ｿ蠖｢蠑, 繧ｯ繝ｩ繧ｹ繧ｿ繝ｪ繝ｳ繧ｰ, 謨ｰ蛟､莠域ｸｬ
6. 螻樊ｧ縺ｮ蝙九→螟画鋤, 蜷咲ｾｩ驥, 鬆蠎城, 髢馴囈驥, 豈秘
7. 遏･隴倩｡ｨ迴ｾ, 豎ｺ螳壽惠, 蛻鬘槭Ν繝ｼ繝ｫ, 荳｡閠縺ｮ螟画鋤, 繝ｫ繝ｼ繝ｫ縺ｮ隗｣驥
8. 逶ｸ髢｢繝ｫ繝ｼ繝ｫ, 莠倶ｾ九吶ｼ繧ｹ縺ｮ陦ｨ迴ｾ, 繧ｯ繝ｩ繧ｹ繧ｿ縺ｮ陦ｨ迴ｾ
9. 蝓ｺ譛ｬ逧縺ｪ蟄ｦ鄙偵い繝ｫ繧ｴ繝ｪ繧ｺ繝, Naive Bayes
10. 豎ｺ螳壽惠, 諠蝣ｱ蛻ｩ蠕, 蛻ｩ蠕玲ｯ
11. 繧ｫ繝舌ｼ繝ｪ繝ｳ繧ｰ繧｢繝ｫ繧ｴ繝ｪ繧ｺ繝, 繝ｫ繝ｼ繝ｫ縺ｨ豎ｺ螳壽惠
12. 蟄ｦ鄙堤ｵ先棡縺ｮ隧穂ｾ｡, 繧ｯ繝ｭ繧ｹ繝舌Μ繝繝ｼ繧ｷ繝ｧ繝ｳ
13. t-讀懷ｮ, 譛蟆剰ｨ倩ｿｰ髟ｷ蜴溽炊
14. 縺ｾ縺ｨ繧
Textbook and reference
蜿り譖ｸ遲
1. Data Mining: Practical Machine Learning Tools and Techniques with Java
Implementations, I. H. Witten, E. Frank, Morgan Kaufmann, 1999.
Related and/or prerequisite courses
竊蝉ｺｺ蟾･遏･閭ｽ迚ｹ隲
Evaluation
繝ｬ繝昴ｼ繝医♀繧医ｳ隰帷ｾｩ縺ｸ縺ｮ雋｢迪ｮ縺ｫ繧医ｊ邱丞粋逧縺ｫ隧穂ｾ｡縺吶ｋｼ
Supplement
遏･隴倥Θ繝九ャ繝
繝ｫ繝ｼ繝ｫ縲∵ｱｺ螳壽惠縲√け繝ｩ繧ｹ繧ｿ繝ｪ繝ｳ繧ｰ縲√ョ繝ｼ繧ｿ繝槭う繝九Φ繧ｰ縲∝ｭｦ鄙偵∵耳隲悶∝撫鬘瑚ｧ｣豎ｺ縲∫衍隴
陦ｨ迴ｾ縲∫匱隕
