AI Safety, Reliability va Security

Kurs haqida

Kurs AI Safety'ni muhandislik va tadqiqot vazifasi sifatida ko'rib chiqadi: adversarial robustness va jailbreak-hujumlarning texnik jihatlaridan tortib, alignment va governance'ning tizimli masalalarigacha. Ham himoya texnikalari (red teaming, constitutional AI, interpretability), ham regulyator framework'lar (EU AI Act, NIST AI RMF) o'rganiladi. Kurs AI tizimlarini mas'uliyatli deploy qilishga tayyorlaydi va karyeraning istalgan yo'nalishi uchun dolzarb.

Nimalarni o'rganasiz

✓LLM-tizimining red team assessment'ini o'tkazish va topilgan zaifliklarni hujjatlashtirish

✓ML-pipeline'ga miqdoriy kafolatlar bilan differensial maxfiylikni qo'llash

✓AI tizimini EU AI Act va NIST AI RMF talablariga muvofiqligini baholash

✓Adversarial attack'lardan asosiy himoyani (adversarial training yoki certified defense) amalga oshirish

Asosiy mavzular

•Adversarial examples: hujumlar (FGSM, PGD, AutoAttack) va himoyalar (adversarial training, certified defenses)

•Prompt injection, LLM'ni jailbreaking qilish: mexanizmlar va mitigation

•Alignment: RLHF, Constitutional AI, scalable oversight

•Interpretability va mechanistic interpretability: circuits, features

•AI tizimlarini red teaming qilish: metodologiya va vositalar

•ML'da Privacy: differensial maxfiylik (DP), federated learning, membership inference

•Regulyator framework'lar: EU AI Act, NIST AI RMF, ISO/IEC 42001

•ML'ga supply chain attack'lar: data poisoning, model poisoning

•AI governance va model cards, system cards

Ushbu tavsif avtomatik tarzda yaratilgan va hali o'qituvchi tomonidan tekshirilmagan — bu muhokama uchun qoralama.

Kurs haqida

Nimalarni o'rganasiz

Asosiy mavzular

Fikr-mulohaza