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Machine Learning CTF
Awareness: Demonstrating Capabilities
ML 100:
Machine Learning with TensorFlow (65 pts extra)
ML 101:
Computer Vision (10 pts extra)
ML 102:
Breaking a CAPTCHA (10 pts extra)
ML 103:
Deblurring Images (40 pts extra)
Technical: Inner Components
ML 104:
Analyzing Input Data (20 pts extra)
ML 105:
Classification (15 pts extra)
ML 106:
Data Poisoning (10 pts extra)
Attacks
ML 107:
Evasion Attack with SecML (40 pts extra)
ML 108:
Evasion Attack on MNIST dataset (40 pts extra)
ML 109:
Poisoning Labels with SecML (30 pts extra)
ML 110:
Poisoning by Gradients (40 pts extra)
ML 111:
Poisoning the MNIST datase (40 pts extra)
Large Language Models
ML 120:
Bloom LLM (30 pts extra)
ML 121:
Prompt Engineering Concepts (20 pts extra)
ML 122:
Comparing LLMs on Colab (20 pts extra)
ML 130:
Prompt Injection (95 pts extra)
Under Development
- The Cleverhans attack library
- Deep Neural Rejection (a defense mechanism)
- Detecting Malware
- Linear and polynomial regression
- Overfitting and underfitting
References
SecML:
Secure and Explainable Machine Learning in Python
ChatGPT Prompt Engineering for Developers
Prompt Engineering Guide
Google's Generative AI learning path
Archived Scores
Spring 2023
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