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説明 ファイル Colab
  01_01_intro_handson.ipynb Open in Colab
  01_02_simple_linear_ols_regression.ipynb Open in Colab
  01_03_cucumber_dataset.ipynb Open in Colab
  01_04_feature_map_HOG.ipynb Open in Colab
  01_05_linear_ols_regression.ipynb Open in Colab
  01_06_overfit_linear_model.ipynb Open in Colab
  01_07_learning_curve_overfitting.ipynb Open in Colab
  01_08_l2_regularized_linear_regression.ipynb Open in Colab
  01_09_validation_curve.ipynb Open in Colab
  01_10_hyperparameter_search.ipynb Open in Colab
  01_11_preliminary_probability.ipynb Open in Colab
  01_12_quantile_regression.ipynb Open in Colab
  01_13_binary_classification.ipynb Open in Colab
  01_14_multiclass_classification.ipynb Open in Colab
  01_15_logsumexp.ipynb Open in Colab
  01_16_neural_network.ipynb Open in Colab
  01_17_neural_network_training.ipynb Open in Colab
  01_18_kNN.ipynb Open in Colab
  01_19_gaussian_kernel.ipynb Open in Colab
  01_20_unstructured_data.ipynb Open in Colab
  01_21_deep_learning.ipynb Open in Colab
  01_22_vanishing_gradient.ipynb Open in Colab
  01_23_loss_landscape.ipynb Open in Colab
  01_24_interpolating_predictors.ipynb Open in Colab
  01_25_curse_of_dimensionality.ipynb Open in Colab
  01_26_scale_distance_and_representation.ipynb Open in Colab
  01_27_similarity_learning.ipynb Open in Colab
  01_28_softmax_with_temperature.ipynb Open in Colab
  01_29_distribution_shift.ipynb Open in Colab
  01_30_class_imbalanced_classification.ipynb Open in Colab
  02_02_image_generation.ipynb Open in Colab
  02_03_upasmpling_convolution.ipynb Open in Colab
  02_04_image_object_removal.ipynb Open in Colab
  02_05_natural_language_processing_base_tasks.ipynb Open in Colab
  04_01_Bayes_parameter_distributions.ipynb Open in Colab
  07_01_Gaussian_mixture.ipynb Open in Colab
  08_01_network_data.ipynb Open in Colab
  09_01_tokenizers.ipynb Open in Colab

参考文献一覧

※全てを片っ端から読もうとすることはお勧めしません（教科書的な文献を除く）。知りたい事柄に応じて必要な箇所を読みましょう。

AIと社会-AI関連法規・AI関連市場 (.bib)

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  2. Bloomberg. (2024). Generative AI 2024 report: assessing opportunities and disruptions in an evolving trillion-dollar market. https://www.bloomberg.com/professional/products/bloomberg-terminal/research/bloomberg-intelligence/download/generative-ai-2024-report/
  3. Hu, K., & Hu, K. (2023). ChatGPT sets record for fastest-growing user base - analyst note. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
  4. NVIDIA announces financial results for first quarter fiscal 2026. (2025). NVIDIA Newsroom. https://nvidianews.nvidia.com/news/nvidia-announces-financial-results-for-first-quarter-fiscal-2026
  5. 人工知能関連技術の研究開発及び活用の推進に関する法律, (2025). https://laws.e-gov.go.jp/law/507AC0000000053
  6. 日本放送協会. (2025). 「性的ディープフェイク」で行政罰の条例改正案を可決 鳥取県. NHKニュース. https://www3.nhk.or.jp/news/html/20250630/k10014848661000.html
  7. 特許法. Retrieved July 18, 2025, from https://laws.e-gov.go.jp/law/334AC0000000121#Mp-Ch_1
  8. 特許庁. (2019). 特許・実用新案審査ハンドブック 附属書B 第1章 コンピュータソフトウエア関連発明. https://www.jpo.go.jp/system/laws/rule/guideline/patent/handbook_shinsa/document/index/app_b1.pdf
  9. 文化審議会著作権分科会法制度小委員会. (2024). AIと著作権に関する考え方について. https://www.bunka.go.jp/seisaku/bunkashingikai/chosakuken/pdf/94037901_01.pdf
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  11. 著作権法第十条第三項第三号. Retrieved July 17, 2025, from https://laws.e-gov.go.jp/law/345AC0000000048#Mp-Ch_2-Se_1
  12. 著作権法の一部を改正する法律（平成30年法律第30号）について | 文化庁. Retrieved July 17, 2025, from https://www.bunka.go.jp/seisaku/chosakuken/hokaisei/h30_hokaisei/

AIと社会-倫理・安全・ガバナンス (.bib)

  1. Angwin, J., Larson, J., Mattu, S., Kirchner, L., & ProPublica. (2016). Machine Bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
  2. Chan, C., Ginosar, S., Zhou, T., & Efros, A. (2019). Everybody dance now. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 5932–5941. https://doi.org/10.1109/ICCV.2019.00603
  3. Chapagain, D., Kshetri, N., & Aryal, B. (2024). Deepfake disasters: a comprehensive review of technology, ethical concerns, countermeasures, and societal implications. 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), 1–9. https://doi.org/10.1109/ETNCC63262.2024.10767452
  4. Chouldechova, A. (2017). Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data, 5(2), 153–163. https://doi.org/10.1089/big.2016.0047
  5. Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017). Algorithmic decision making and the cost of fairness. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 797–806. https://doi.org/10.1145/3097983.3098095
  6. delving_2025_july.png. (2025). GitHub. https://github.com/berenslab/llm-excess-vocab/blob/main/figures/post-publication-updates/delving_2025_july.png
  7. Fernando, T., Priyasad, D., Sridharan, S., Ross, A., & Fookes, C. (2025). Face deepfakes: a comprehensive review. https://doi.org/10.48550/arXiv.2502.09812
  8. 会計担当が38億円を詐欺グループに送金、ビデオ会議のＣＦＯは偽物. (2024). CNN.co.jp. https://www.cnn.co.jp/world/35214839.html
  9. Kleinberg, J., Mullainathan, S., & Raghavan, M. (2017). Inherent trade-offs in the fair determination of risk scores. In C. H. Papadimitriou (Ed.), 8th Innovations in Theoretical Computer Science Conference (Vol. 67, pp. 43:1–43:23). Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Germany. https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2017.43
  10. Kobak, D., González-Márquez, R., Horvát, E.-Á., & Lause, J. (2025). Delving into LLM-assisted writing in biomedical publications through excess vocabulary. Science Advances, 11(27), eadt3813. https://doi.org/10.1126/sciadv.adt3813
  11. Li, P., Yang, J., Islam, M. A., & Ren, S. (2025). Making AI less ’thirsty.’ Commun. ACM, 68(7), 54–61. https://doi.org/10.1145/3724499
  12. Manzini, A., Keeling, G., Alberts, L., Vallor, S., Morris, M. R., & Gabriel, I. (2024). The code that binds us: navigating the appropriateness of human-AI assistant relationships. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7(1), 943–957. https://doi.org/10.1609/aies.v7i1.31694
  13. Marx, K. (1959). Economic & Philosophic Manuscripts of 1844 (M. Milligan, Tran.). Progress Publishers. https://www.marxists.org/archive/marx/works/1844/manuscripts/preface.htm
  14. Mata v. Avianca, Inc. (Number 1:22-cv-01461). (Number). District Court, S.D. New York. Retrieved July 13, 2025, from https://www.courtlistener.com/docket/63107798/mata-v-avianca-inc/
  15. Moffatt v. Air Canada. (2024). In CanLII (Vol. 149, Number SC-2023-005609). BCCRT. https://canlii.ca/t/k2spq
  16. OWASP. (2024). OWASP Top 10 for LLM applications & generative AI (Technical Report OWASP PDF v4.2.0a 20241114-202703; Number OWASP PDF v4.2.0a 20241114-202703).
  17. 生成AIでランサムウェアを作成した容疑者の摘発事例を考察. (2025). Trend Micro. https://www.trendmicro.com/ja_jp/jp-security/24/e/breaking-securitynews-20240529-02.html
  18. 生成ＡＩ悪用し楽天モバイルに不正アクセス、１０００件以上の回線入手し転売か…容疑で中高生３人逮捕. (2025). 読売新聞オンライン. https://www.yomiuri.co.jp/national/20250226-OYT1T50205/
  19. 生成ＡＩ悪用しウイルス作成、有罪判決…ＩＴ知識なくとも「１か月ぐらいで簡単に作れた」. (2024). 読売新聞オンライン. https://www.yomiuri.co.jp/national/20241025-OYT1T50209/
  20. Storchan, V., Kumar, R., Chowdhury, R., Goldfarb-Tarrant, S., & Cattell, S. (2024). Generative AI red teaming challenge: transparency report [Technical Report]. DEF CON. https://drive.google.com/file/d/1JqpbIP6DNomkb32umLoiEPombK2-0Rc-/view
  21. Tabassi, E. (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). National Institute of Standards and Technology. https://doi.org/10.6028/nist.ai.100-1
  22. Weidinger, L., Barnhart, J., Brennan, J., Butterfield, C., Young, S., Hawkins, W., Hendricks, L. A., Comanescu, R., Chang, O., Rodriguez, M., Beroshi, J., Bloxwich, D., Proleev, L., Chen, J., Farquhar, S., Ho, L., Gabriel, I., Dafoe, A., & Isaac, W. (2024). Holistic safety and responsibility evaluations of advanced AI models. https://doi.org/10.48550/arXiv.2404.14068
  23. Writers Guild of America. (2023). Summary of the 2023 WGA MBA. WGA Contract 2023. https://www.wgacontract2023.org/the-campaign/summary-of-the-2023-wga-mba

AIと社会-実社会応用の事例-分類 (.bib)

    AIと社会-実社会応用の事例-回帰 (.bib)

    1. DeepETA: How Uber Predicts Arrival Times Using Deep Learning. (2022). Uber Blog. https://www.uber.com/blog/deepeta-how-uber-predicts-arrival-times/
    2. GO株式会社. (2023). タクシーアプリ『GO』の データ基盤の全体像. https://www.slideshare.net/slideshow/ss-258369181/258369181
    3. Ye, P., Qian, J., Chen, J., Wu, C.-hung, Zhou, Y., De Mars, S., Yang, F., & Zhang, L. (2018). Customized Regression Model for Airbnb Dynamic Pricing. 932–940. https://doi.org/10.1145/3219819.3219830

    AIと社会-実社会応用の事例 (.bib)

    1. DeepETA: How Uber Predicts Arrival Times Using Deep Learning. (2022). Uber Blog. https://www.uber.com/blog/deepeta-how-uber-predicts-arrival-times/
    2. GO株式会社. (2023). タクシーアプリ『GO』の データ基盤の全体像. https://www.slideshare.net/slideshow/ss-258369181/258369181
    3. Ye, P., Qian, J., Chen, J., Wu, C.-hung, Zhou, Y., De Mars, S., Yang, F., & Zhang, L. (2018). Customized Regression Model for Airbnb Dynamic Pricing. 932–940. https://doi.org/10.1145/3219819.3219830

    AIと社会-法律・AI関連市場 (.bib)

      AIと社会-生産性と職務の変化 (.bib)

      1. Cui, Z. (K., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2025). The effects of generative AI on high-skilled work: evidence from three field experiments with software developers (Number 4945566) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.4945566
      2. Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. https://doi.org/10.48550/arXiv.2303.10130
      3. Filippucci, F., Gal, P., Jona-Lasinio, C., Leandro, A., & Nicoletti, G. (2024). The impact of artificial intelligence on productivity, distribution and growth: key mechanisms, initial evidence and policy challenges (No.15; OECD Artificial Intelligence Papers, Number 15). OECD Publishing. https://doi.org/10.1787/8d900037-en
      4. Filippucci, F., Gal, P., & Schief, M. (2024). Miracle or myth? Assessing the macroeconomic productivity gains from artificial intelligence (No.29; OECD Artificial Intelligence Papers, Number 29). OECD Publishing. https://doi.org/10.1787/b524a072-en
      5. Handa, K., Tamkin, A., McCain, M., Huang, S., Durmus, E., Heck, S., Mueller, J., Hong, J., Ritchie, S., Belonax, T., Troy, K. K., Amodei, D., Kaplan, J., Clark, J., & Ganguli, D. (2025). Which economic tasks are performed with AI? Evidence from millions of Claude conversations. https://doi.org/10.48550/arXiv.2503.04761

      AIと社会-生産性と職業への影響 (.bib)

      1. Cui, Z. (K., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2025). The effects of generative ai on high-skilled work: evidence from three field experiments with software developers (Number 4945566) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.4945566

      AIと社会-生産性・職務変化・雇用市場 (.bib)

      1. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (Number 31161) [Working Paper]. https://doi.org/10.3386/w31161
      2. Brynjolfsson, E., Mitchell, T., & Rock, D. (2018). What can machines learn, and what does it mean for occupations and the economy? AEA Papers and Proceedings, 108, 43–47. https://doi.org/10.1257/pandp.20181019
      3. Cazzaniga, M. (2024). Gen-AI: artificial intelligence and the future of work. Staff Discussion Notes, 2024(001), 1. https://doi.org/10.5089/9798400262548.006
      4. Cui, Z. (K., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2025). The effects of generative AI on high-skilled work: evidence from three field experiments with software developers (Number 4945566) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.4945566
      5. Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: an early look at the labor market impact potential of large language models. https://doi.org/10.48550/arXiv.2303.10130
      6. Felten, E. W., Raj, M., & Seamans, R. (2023). How will Language Modelers like ChatGPT Affect Occupations and Industries? (Number 4375268) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.4375268
      7. Felten, E. W., Raj, M., & Seamans, R. (2018). A method to link advances in artificial intelligence to occupational abilities. AEA Papers and Proceedings, 108, 54–57. https://doi.org/10.1257/pandp.20181021
      8. Felten, E. W., Raj, M., & Seamans, R. (2023). Occupational heterogeneity in exposure to Generative AI (Number 4414065) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.4414065
      9. Filippucci, F., Gal, P., Jona-Lasinio, C., Leandro, A., & Nicoletti, G. (2024). The impact of artificial intelligence on productivity, distribution and growth: key mechanisms, initial evidence and policy challenges (No.15; OECD Artificial Intelligence Papers, Number 15). OECD Publishing. https://doi.org/10.1787/8d900037-en
      10. Filippucci, F., Gal, P., & Schief, M. (2024). Miracle or myth? Assessing the macroeconomic productivity gains from artificial intelligence (No.29; OECD Artificial Intelligence Papers, Number 29). OECD Publishing. https://doi.org/10.1787/b524a072-en
      11. Handa, K., Tamkin, A., McCain, M., Huang, S., Durmus, E., Heck, S., Mueller, J., Hong, J., Ritchie, S., Belonax, T., Troy, K. K., Amodei, D., Kaplan, J., Clark, J., & Ganguli, D. (2025). Which economic tasks are performed with AI? Evidence from millions of Claude conversations. https://doi.org/10.48550/arXiv.2503.04761
      12. Lane, M. (2024). Who will be the workers most affected by AI? A closer look at the impact of AI on women, low-skilled workers and other groups (Technical Report No.26; Number 26). OECD Publishing. https://doi.org/10.1787/14dc6f89-en
      13. Lipsey, R. G., Carlaw, K. I., & Bekar, C. T. (2005). Economic Transformations: General Purpose Technologies and Long-Term Economic Growth. Oxford University Press. https://doi.org/10.1093/oso/9780199285648.001.0001
      14. Mäkelä, E., & Stephany, F. (2025). Complement or substitute? How AI increases the demand for human skills (Number 5153230) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.5153230
      15. Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187–192. https://doi.org/10.1126/science.adh2586
      16. Peng, S., Kalliamvakou, E., Cihon, P., & Demirer, M. (2023). The impact of AI on developer productivity: evidence from GitHub Copilot. https://doi.org/10.48550/arXiv.2302.06590
      17. Webb, M. (2019). The Impact of Artificial Intelligence on the Labor Market (Number 3482150) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.3482150
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      19. 尭之 新田. 生成ＡＩが描く日本の職業の明暗とその対応策.

      AIと社会 (.bib)

      1. 100,000 H100 clusters: power, network topology, ethernet vs infiniband, reliability, failures, checkpointing. (2024). SemiAnalysis. https://semianalysis.com/2024/06/17/100000-h100-clusters-power-network/
      2. Angwin, J., Larson, J., Mattu, S., Kirchner, L., & ProPublica. (2016). Machine Bias. ProPublica. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing
      3. Bloomberg. (2024). Generative AI 2024 report: assessing opportunities and disruptions in an evolving trillion-dollar market. https://www.bloomberg.com/professional/products/bloomberg-terminal/research/bloomberg-intelligence/download/generative-ai-2024-report/
      4. Brynjolfsson, E., Li, D., & Raymond, L. R. (2023). Generative AI at work (Number 31161) [Working Paper]. https://doi.org/10.3386/w31161
      5. Brynjolfsson, E., Mitchell, T., & Rock, D. (2018). What can machines learn, and what does it mean for occupations and the economy? AEA Papers and Proceedings, 108, 43–47. https://doi.org/10.1257/pandp.20181019
      6. Cazzaniga, M. (2024). Gen-AI: artificial intelligence and the future of work. Staff Discussion Notes, 2024(001), 1. https://doi.org/10.5089/9798400262548.006
      7. Chan, C., Ginosar, S., Zhou, T., & Efros, A. (2019). Everybody dance now. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), 5932–5941. https://doi.org/10.1109/ICCV.2019.00603
      8. Chapagain, D., Kshetri, N., & Aryal, B. (2024). Deepfake disasters: a comprehensive review of technology, ethical concerns, countermeasures, and societal implications. 2024 International Conference on Emerging Trends in Networks and Computer Communications (ETNCC), 1–9. https://doi.org/10.1109/ETNCC63262.2024.10767452
      9. Chouldechova, A. (2017). Fair Prediction with Disparate Impact: A Study of Bias in Recidivism Prediction Instruments. Big Data, 5(2), 153–163. https://doi.org/10.1089/big.2016.0047
      10. Corbett-Davies, S., Pierson, E., Feller, A., Goel, S., & Huq, A. (2017). Algorithmic decision making and the cost of fairness. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 797–806. https://doi.org/10.1145/3097983.3098095
      11. Cui, Z. (K., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2025). The effects of generative AI on high-skilled work: evidence from three field experiments with software developers (Number 4945566) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.4945566
      12. DeepETA: How Uber Predicts Arrival Times Using Deep Learning. (2022). Uber Blog. https://www.uber.com/blog/deepeta-how-uber-predicts-arrival-times/
      13. delving_2025_july.png. (2025). GitHub. https://github.com/berenslab/llm-excess-vocab/blob/main/figures/post-publication-updates/delving_2025_july.png
      14. Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: an early look at the labor market impact potential of large language models. https://doi.org/10.48550/arXiv.2303.10130
      15. Felten, E. W., Raj, M., & Seamans, R. (2023). How will Language Modelers like ChatGPT Affect Occupations and Industries? (Number 4375268) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.4375268
      16. Felten, E. W., Raj, M., & Seamans, R. (2018). A method to link advances in artificial intelligence to occupational abilities. AEA Papers and Proceedings, 108, 54–57. https://doi.org/10.1257/pandp.20181021
      17. Felten, E. W., Raj, M., & Seamans, R. (2023). Occupational heterogeneity in exposure to Generative AI (Number 4414065) [SSRN Scholarly Paper]. https://doi.org/10.2139/ssrn.4414065
      18. Fernando, T., Priyasad, D., Sridharan, S., Ross, A., & Fookes, C. (2025). Face deepfakes: a comprehensive review. https://doi.org/10.48550/arXiv.2502.09812
      19. Filippucci, F., Gal, P., Jona-Lasinio, C., Leandro, A., & Nicoletti, G. (2024). The impact of artificial intelligence on productivity, distribution and growth: key mechanisms, initial evidence and policy challenges (No.15; OECD Artificial Intelligence Papers, Number 15). OECD Publishing. https://doi.org/10.1787/8d900037-en
      20. Filippucci, F., Gal, P., & Schief, M. (2024). Miracle or myth? Assessing the macroeconomic productivity gains from artificial intelligence (No.29; OECD Artificial Intelligence Papers, Number 29). OECD Publishing. https://doi.org/10.1787/b524a072-en
      21. GO株式会社. (2023). タクシーアプリ『GO』の データ基盤の全体像. https://www.slideshare.net/slideshow/ss-258369181/258369181
      22. Handa, K., Tamkin, A., McCain, M., Huang, S., Durmus, E., Heck, S., Mueller, J., Hong, J., Ritchie, S., Belonax, T., Troy, K. K., Amodei, D., Kaplan, J., Clark, J., & Ganguli, D. (2025). Which economic tasks are performed with AI? Evidence from millions of Claude conversations. https://doi.org/10.48550/arXiv.2503.04761
      23. Hu, K., & Hu, K. (2023). ChatGPT sets record for fastest-growing user base - analyst note. Reuters. https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
      24. 会計担当が38億円を詐欺グループに送金、ビデオ会議のＣＦＯは偽物. (2024). CNN.co.jp. https://www.cnn.co.jp/world/35214839.html
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              5. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. (2022). Engineering Applications of Artificial Intelligence, 110, 104743. https://doi.org/10.1016/j.engappai.2022.104743
              6. Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining.
              7. Fraley, C., & Raftery, A. E. (1998). How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal, 41(8), 578–588. https://doi.org/10.1093/comjnl/41.8.578
              8. Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3), 283–304. https://doi.org/10.1023/A:1009769707641
              9. Kamvar, S. D., Klein, D., & Manning, C. D. (2002). Interpreting and extending classical agglomerative clustering algorithms using a model-based approach. Proceedings of the 19th International Conference on Machine Learning, 283–290.
              10. Miyamoto, S. (2012). Inductive and non-inductive methods of clustering. 2012 IEEE International Conference on Granular Computing, 12–17. https://doi.org/10.1109/GrC.2012.6468710
              11. Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst., 42(3), 19:1–19:21. https://doi.org/10.1145/3068335
              12. SIGKDD News: 2014 SIGKDD Test of Time Award. (2014). https://www.kdd.org/News/view/2014-sigkdd-test-of-time-award
              13. Ward Jr., J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845
              14. Xu, D., & Tian, Y. (2015). A Comprehensive Survey of Clustering Algorithms. Annals of Data Science, 2(2), 165–193. https://doi.org/10.1007/s40745-015-0040-1

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              1. Elad, M. (2010). Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer. https://doi.org/10.1007/978-1-4419-7011-4
              2. 冨岡 亮太. (2015). スパース性に基づく機械学習 = Machine learning with sparsity inducing regularizations （MLP機械学習プロフェッショナルシリーズ） (講談社サイエンティフィク, Ed.). 講談社. https://elib.maruzen.co.jp/elib/html/BookDetail/Id/3000080942/

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              1. Manning, C. D., Prabhakar Raghavan, & Schütze, H. (2009). An Introduction to Information Retrieval. Cambridge University Press.
              2. 石井 健一郎, & 上田 修功. (2014). 教師なし学習入門 （わかりやすいパターン認識 続）. オーム社.

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              1. Ge, D., Jiang, X., & Ye, Y. (2011). A note on the complexity of Lpminimization. Mathematical Programming, 129(2), 285–299. https://doi.org/10.1007/s10107-011-0470-2
              2. Natarajan, B. K. (1995). Sparse Approximate Solutions to Linear Systems. SIAM Journal on Computing, 24(2), 227–234. https://doi.org/10.1137/S0097539792240406

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              1. Campbell, N. D. F., & Kautz, J. (2014). Learning a manifold of fonts. ACM Transactions on Graphics, 33(4), 1–11. https://doi.org/10.1145/2601097.2601212
              2. Jolliffe, I. T. (2010). Principal Component Analysis (2nd ed.). Springer.
              3. Ma, Y., & Fu, Y. (2012). Manifold Learning Theory and Applications. CRC Press. http://www.amazon.com/Manifold-Learning-Theory-Applications-Yunqian/dp/1439871094/ref=pd_sim_sbs_b_1?ie=UTF8&refRID=1E2Y5KJQ1MJ48BK5C7BT

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              1. Bouman, R., Bukhsh, Z., & Heskes, T. (2024). Unsupervised anomaly detection algorithms on real-world data: how many do we need? Journal of Machine Learning Research, 25, 1–34.
              2. Hariri, S., Kind, M. C., & Brunner, R. J. (2021). Extended isolation forest. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1479–1489. https://doi.org/10.1109/TKDE.2019.2947676
              3. Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2012). Isolation-Based Anomaly Detection. ACM Trans. Knowl. Discov. Data, 6(1), 3:1–3:39. https://doi.org/10.1145/2133360.2133363
              4. Pelletier, B. (2024). On the statistical properties of the isolation forest anomaly detection method. https://hal.science/hal-04430185

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              1. Ahmad, A., & Khan, S. S. (2019). Survey of state-of-the-art mixed data clustering algorithms. IEEE Access, 7, 31883–31902. https://doi.org/10.1109/ACCESS.2019.2903568
              2. Arthur, D., & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035.
              3. Bouman, R., Bukhsh, Z., & Heskes, T. (2024). Unsupervised anomaly detection algorithms on real-world data: how many do we need? Journal of Machine Learning Research, 25, 1–34.
              4. C. Fraley, & A. Raftery. (2000). Model-based clustering, discriminant analysis, and density estimation (Technical Report No.380; Number 380). Department of Statistics, University of Washington. https://csss.uw.edu/files/working-papers/2000/wp11.pdf
              5. Campbell, N. D. F., & Kautz, J. (2014). Learning a manifold of fonts. ACM Transactions on Graphics, 33(4), 1–11. https://doi.org/10.1145/2601097.2601212
              6. Cao, F., Liang, J., & Bai, L. (2009). A new initialization method for categorical data clustering. Expert Systems with Applications, 36(7), 10223–10228. https://doi.org/10.1016/j.eswa.2009.01.060
              7. A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. (2022). Engineering Applications of Artificial Intelligence, 110, 104743. https://doi.org/10.1016/j.engappai.2022.104743
              8. Elad, M. (2010). Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer. https://doi.org/10.1007/978-1-4419-7011-4
              9. Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining.
              10. Fraley, C., & Raftery, A. E. (1998). How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal, 41(8), 578–588. https://doi.org/10.1093/comjnl/41.8.578
              11. 冨岡 亮太. (2015). スパース性に基づく機械学習 = Machine learning with sparsity inducing regularizations （MLP機械学習プロフェッショナルシリーズ） (講談社サイエンティフィク, Ed.). 講談社. https://elib.maruzen.co.jp/elib/html/BookDetail/Id/3000080942/
              12. Ge, D., Jiang, X., & Ye, Y. (2011). A note on the complexity of Lpminimization. Mathematical Programming, 129(2), 285–299. https://doi.org/10.1007/s10107-011-0470-2
              13. Hariri, S., Kind, M. C., & Brunner, R. J. (2021). Extended isolation forest. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1479–1489. https://doi.org/10.1109/TKDE.2019.2947676
              14. Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3), 283–304. https://doi.org/10.1023/A:1009769707641
              15. Jolliffe, I. T. (2010). Principal Component Analysis (2nd ed.). Springer.
              16. Kamvar, S. D., Klein, D., & Manning, C. D. (2002). Interpreting and extending classical agglomerative clustering algorithms using a model-based approach. Proceedings of the 19th International Conference on Machine Learning, 283–290.
              17. Lee, J. D., Sun, D. L., Sun, Y., & Taylor, J. E. (2016). Exact Post-Selection Inference, with Application to the Lasso. The Annals of Statistics, 44(3), 907–927. https://www.jstor.org/stable/43818915
              18. Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2012). Isolation-Based Anomaly Detection. ACM Trans. Knowl. Discov. Data, 6(1), 3:1–3:39. https://doi.org/10.1145/2133360.2133363
              19. Ma, Y., & Fu, Y. (2012). Manifold Learning Theory and Applications. CRC Press. http://www.amazon.com/Manifold-Learning-Theory-Applications-Yunqian/dp/1439871094/ref=pd_sim_sbs_b_1?ie=UTF8&refRID=1E2Y5KJQ1MJ48BK5C7BT
              20. Manning, C. D., Prabhakar Raghavan, & Schütze, H. (2009). An Introduction to Information Retrieval. Cambridge University Press.
              21. Miyamoto, S. (2012). Inductive and non-inductive methods of clustering. 2012 IEEE International Conference on Granular Computing, 12–17. https://doi.org/10.1109/GrC.2012.6468710
              22. Natarajan, B. K. (1995). Sparse Approximate Solutions to Linear Systems. SIAM Journal on Computing, 24(2), 227–234. https://doi.org/10.1137/S0097539792240406
              23. Pelletier, B. (2024). On the statistical properties of the isolation forest anomaly detection method. https://hal.science/hal-04430185
              24. Schubert, E., Sander, J., Ester, M., Kriegel, H. P., & Xu, X. (2017). DBSCAN revisited, revisited: why and how you should (still) use DBSCAN. ACM Trans. Database Syst., 42(3), 19:1–19:21. https://doi.org/10.1145/3068335
              25. 石井 健一郎, & 上田 修功. (2014). 教師なし学習入門 （わかりやすいパターン認識 続）. オーム社.
              26. SIGKDD News: 2014 SIGKDD Test of Time Award. (2014). https://www.kdd.org/News/view/2014-sigkdd-test-of-time-award
              27. Ward Jr., J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845
              28. Xu, D., & Tian, Y. (2015). A Comprehensive Survey of Clustering Algorithms. Annals of Data Science, 2(2), 165–193. https://doi.org/10.1007/s40745-015-0040-1

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              2. Dathathri, S., See, A., Ghaisas, S., Huang, P.-S., McAdam, R., Welbl, J., Bachani, V., Kaskasoli, A., Stanforth, R., Matejovicova, T., Hayes, J., Vyas, N., Merey, M. A., Brown-Cohen, J., Bunel, R., Balle, B., Cemgil, T., Ahmed, Z., Stacpoole, K., … Kohli, P. (2024). Scalable watermarking for identifying large language model outputs. Nature, 634(8035), 818–823. https://doi.org/10.1038/s41586-024-08025-4
              3. Gottesman, Y. (2023). Understand diffusion models with VAEs. Yoni Gottesman. https://yonigottesman.github.io/2023/03/11/vae.html
              4. Ho, J., Jain, A., & Abbeel, P. Denoising diffusion probabilistic models.
              5. Song, Y., & Ermon, S. (2019). Generative modeling by estimating gradients of the data distribution. In Proceedings of the 33rd International Conference on Neural Information Processing Systems (Number 1067, pp. 11918–11930). Curran Associates Inc.
              6. Song, Y. (2021). Generative modeling by estimating gradients of the data distribution. Yang Song. https://yang-song.net/blog/2021/score
              7. Song, Y., Durkan, C., Murray, I., & Ermon, S. (2021, November 9). Maximum likelihood training of score-based diffusion models. https://openreview.net/forum?id=AklttWFnxS9
              8. Vincent, P. (2011). A connection between score matching and denoising autoencoders. Neural Computation, 23(7), 1661–1674. https://doi.org/10.1162/NECO_a_00142

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              2. Arthur, D., & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035.
              3. C. Fraley, & A. Raftery. (2000). Model-based clustering, discriminant analysis, and density estimation (Technical Report No.380; Number 380). Department of Statistics, University of Washington. https://csss.uw.edu/files/working-papers/2000/wp11.pdf
              4. Cao, F., Liang, J., & Bai, L. (2009). A new initialization method for categorical data clustering. Expert Systems with Applications, 36(7), 10223–10228. https://doi.org/10.1016/j.eswa.2009.01.060
              5. Fraley, C., & Raftery, A. E. (1998). How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal, 41(8), 578–588. https://doi.org/10.1093/comjnl/41.8.578
              6. Huang, Z. (1998). Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Mining and Knowledge Discovery, 2(3), 283–304. https://doi.org/10.1023/A:1009769707641
              7. Kamvar, S. D., Klein, D., & Manning, C. D. (2002). Interpreting and extending classical agglomerative clustering algorithms using a model-based approach. Proceedings of the 19th International Conference on Machine Learning, 283–290.
              8. Miyamoto, S. (2012). Inductive and non-inductive methods of clustering. 2012 IEEE International Conference on Granular Computing, 12–17. https://doi.org/10.1109/GrC.2012.6468710
              9. Ward Jr., J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845

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              2. 冨岡 亮太. (2015). スパース性に基づく機械学習 = Machine learning with sparsity inducing regularizations （MLP機械学習プロフェッショナルシリーズ） (講談社サイエンティフィク, Ed.). 講談社. https://elib.maruzen.co.jp/elib/html/BookDetail/Id/3000080942/

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              1. Ge, D., Jiang, X., & Ye, Y. (2011). A note on the complexity of Lpminimization. Mathematical Programming, 129(2), 285–299. https://doi.org/10.1007/s10107-011-0470-2
              2. Natarajan, B. K. (1995). Sparse Approximate Solutions to Linear Systems. SIAM Journal on Computing, 24(2), 227–234. https://doi.org/10.1137/S0097539792240406

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              1. Campbell, N. D. F., & Kautz, J. (2014). Learning a manifold of fonts. ACM Transactions on Graphics, 33(4), 1–11. https://doi.org/10.1145/2601097.2601212
              2. Jolliffe, I. T. (2010). Principal Component Analysis (2nd ed.). Springer.
              3. Ma, Y., & Fu, Y. (2012). Manifold Learning Theory and Applications. CRC Press. http://www.amazon.com/Manifold-Learning-Theory-Applications-Yunqian/dp/1439871094/ref=pd_sim_sbs_b_1?ie=UTF8&refRID=1E2Y5KJQ1MJ48BK5C7BT

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              1. Bouman, R., Bukhsh, Z., & Heskes, T. (2024). Unsupervised anomaly detection algorithms on real-world data: how many do we need? Journal of Machine Learning Research, 25, 1–34.
              2. Hariri, S., Kind, M. C., & Brunner, R. J. (2021). Extended isolation forest. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1479–1489. https://doi.org/10.1109/TKDE.2019.2947676
              3. Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2012). Isolation-Based Anomaly Detection. ACM Trans. Knowl. Discov. Data, 6(1), 3:1–3:39. https://doi.org/10.1145/2133360.2133363

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              1. Ahmad, A., & Khan, S. S. (2019). Survey of state-of-the-art mixed data clustering algorithms. IEEE Access, 7, 31883–31902. https://doi.org/10.1109/ACCESS.2019.2903568
              2. Arthur, D., & Vassilvitskii, S. (2007). k-means++: the advantages of careful seeding. Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, 1027–1035.
              3. Bouman, R., Bukhsh, Z., & Heskes, T. (2024). Unsupervised anomaly detection algorithms on real-world data: how many do we need? Journal of Machine Learning Research, 25, 1–34.
              4. C. Fraley, & A. Raftery. (2000). Model-based clustering, discriminant analysis, and density estimation (Technical Report No.380; Number 380). Department of Statistics, University of Washington. https://csss.uw.edu/files/working-papers/2000/wp11.pdf
              5. Campbell, N. D. F., & Kautz, J. (2014). Learning a manifold of fonts. ACM Transactions on Graphics, 33(4), 1–11. https://doi.org/10.1145/2601097.2601212
              6. Cao, F., Liang, J., & Bai, L. (2009). A new initialization method for categorical data clustering. Expert Systems with Applications, 36(7), 10223–10228. https://doi.org/10.1016/j.eswa.2009.01.060
              7. Elad, M. (2010). Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. Springer. https://doi.org/10.1007/978-1-4419-7011-4
              8. Fraley, C., & Raftery, A. E. (1998). How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis. The Computer Journal, 41(8), 578–588. https://doi.org/10.1093/comjnl/41.8.578
              9. 冨岡 亮太. (2015). スパース性に基づく機械学習 = Machine learning with sparsity inducing regularizations （MLP機械学習プロフェッショナルシリーズ） (講談社サイエンティフィク, Ed.). 講談社. https://elib.maruzen.co.jp/elib/html/BookDetail/Id/3000080942/
              10. Ge, D., Jiang, X., & Ye, Y. (2011). A note on the complexity of Lpminimization. Mathematical Programming, 129(2), 285–299. https://doi.org/10.1007/s10107-011-0470-2
              11. Hariri, S., Kind, M. C., & Brunner, R. J. (2021). Extended isolation forest. IEEE Transactions on Knowledge and Data Engineering, 33(4), 1479–1489. https://doi.org/10.1109/TKDE.2019.2947676
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              21. Ward Jr., J. H. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58(301), 236–244. https://doi.org/10.1080/01621459.1963.10500845

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