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

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Linkedin - Machine Learning and Artificial Intelligence Security Risk - Categorizing Attacks and Failure Modes (Size: 713.3 MB)
  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 614.4 B
  002. What you should know.mp4 3.6 MB
  003. How systems can fail and how to protect th.en.srt 5 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
  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 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 MB
  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 MB
  019. Overfitting underfit.en.srt 4.2 KB
  019. Overfitting underfit.mp4 23.8 MB
  020. Data bias considerat.en.srt 7 KB
  020. Data bias considerat.mp4 41.5 MB
  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 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
  025. Next steps.en.srt 2.2 KB
  025. Next steps.mp4 19 MB
  Bonus Resources.txt 409.6 B
  Get Bonus Downloads Here.url 204.8 B
  References.pdf 43.6 KB
  ▲ 53 total files

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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