Linkedin - Machine Learning and Artificial Intelligence Security Risk - Categorizing Attacks and Failure Modes

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[ CourseHulu.com ] Linkedin - Machine Learning and Artificial Intelligence Security Risk - Categorizing Attacks and Failure Modes
  • Get Bonus Downloads Here.url (0.2 KB)
    • ~Get Your Files Here ! 1. Introduction
    • 001. Machine learning security concerns.en.srt (1.8 KB)
    • 001. Machine learning security concerns.mp4 (16.3 MB)
    • 002. What you should know.en.srt (0.6 KB)
    • 002. What you should know.mp4 (3.6 MB)
    2. Machine Learning Foundations
    • 003. How systems can fail and how to protect th.en.srt (5.0 KB)
    • 003. How systems can fail and how to protect th.mp4 (40.2 MB)
    • 004. Why does ML security matter.en.srt (8.9 KB)
    • 004. Why does ML security matter.mp4 (68.2 MB)
    • 005. Attacks vs. unintentional failure modes.en.srt (4.6 KB)
    • 005. Attacks vs. unintentional failure modes.mp4 (25.8 MB)
    • 006. Security goals for ML CIA.en.srt (4.6 KB)
    • 006. Security goals for ML CIA.mp4 (32.7 MB)
    3. Intentional Failure Modes Attacks
    • 007. Perturbation attacks and AUPs.en.srt (5.4 KB)
    • 007. Perturbation attacks and AUPs.mp4 (41.6 MB)
    • 008. Poisoning attacks.en.srt (5.1 KB)
    • 008. Poisoning attacks.mp4 (27.4 MB)
    • 009. Reprogramming neural nets.en.srt (2.6 KB)
    • 009. Reprogramming neural nets.mp4 (14.2 MB)
    • 010. Physical domain (3D adversarial objec.en.srt (3.6 KB)
    • 010. Physical domain (3D adversarial objec.mp4 (30.6 MB)
    • 011. Supply chain attacks.en.srt (4.2 KB)
    • 011. Supply chain attacks.mp4 (23.3 MB)
    • 012. Model inversion.en.srt (4.7 KB)
    • 012. Model inversion.mp4 (27.6 MB)
    • 013. System manipulation.en.srt (4.2 KB)
    • 013. System manipulation.mp4 (36.4 MB)
    • 014. Membership inference and model steali.en.srt (3.0 KB)
    • 014. Membership inference and model steali.mp4 (17.6 MB)
    • 015. Backdoors and existing exploits.en.srt (3.6 KB)
    • 015. Backdoors and existing exploits.mp4 (20.0 MB)
    4. Unintentional Failure Modes Intrinsic Design Flaws
    • 016. Reward hacking.en.srt (3.4 KB)
    • 016. Reward hacking.mp4 (27.2 MB)
    • 017. Side effects in rein.en.srt (3.8 KB)
    • 017. Side effects in rein.mp4 (21.6 MB)
    • 018. Distributional shift.en.srt (4.5 KB)
    • 018. Distributional shift.mp4 (26.0 MB)
    • 019. Overfitting underfit.en.srt (4.2 KB)
    • 019. Overfitting underfit.mp4 (23.8 MB)
    • 020. Data bias considerat.en.srt (7.0 KB)
    • 020. Data bias considerat.mp4 (41.5 MB)
    5. Building Resilient ML
    • 021. Effective techniques for building resilience in M.en.srt (3.7 KB)
    • 021. Effective techniques for building resilience in M.mp4 (30.1 MB)
    • 022. ML dataset hygiene.en.srt (6.8 KB)
    • 022. ML dataset hygiene.mp4 (38.3 MB)
    • 023. ML adversarial training.en.srt (6.0 KB)
    • 023. ML adversarial training.mp4 (34.8 MB)
    • 024. ML access control to APIs.en.srt (4.1 KB)
    • 024. ML access control to APIs.mp4 (25.3 MB)
    6. Conclusion
    • 025. Next steps.en.srt (2.2 KB)
    • 025. Next steps.mp4 (19.0 MB)
    • Bonus Resources.txt (0.4 KB)
      • Ex_Files_ML_and_AI_Security_Risk_Categorizing_Attacks Exercise Files
      • References.pdf (43.6 KB)

Description

Machine Learning and Artificial Intelligence Security Risk: Categorizing Attacks and Failure Modes



https://CourseHulu.com

LinkedIn Learning
Duration: 1h 11m | .MP4 1280x720, 30 fps(r) | AAC, 48000 Hz, 2ch | 713 MB
Genre: eLearning | Language: English

From predicting medical outcomes to managing retirement funds, we put a lot of trust in machine learning (ML) and artificial intelligence (AI) technology, even though we know they are vulnerable to attacks, and that sometimes they can completely fail us. In this course, instructor Diana Kelley pulls real-world examples from the latest ML research and walks through ways that ML and AI can fail, providing pointers on how to design, build, and maintain resilient systems.

Learn about intentional failures caused by attacks and unintentional failures caused by design flaws and implementation issues. Security threats and privacy risks are serious, but with the right tools and preparation you can set yourself up to reduce them. Diana explains some of the most effective approaches and techniques for building robust and resilient ML, such as dataset hygiene, adversarial training, and access control to APIs.



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Linkedin - Machine Learning and Artificial Intelligence Security Risk - Categorizing Attacks and Failure Modes

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713.3 MB
seeders:4
leechers:7
Linkedin - Machine Learning and Artificial Intelligence Security Risk - Categorizing Attacks and Failure Modes

Torrent hash: 8E79C85312BFCD10422B0C59CDA766C39E8A84BF