AI Updates: Security, Neural Networks & Intelligent Agents
Applied AI is a hands-on course that teaches core AI concepts and real-world applications — from
neural networks to intelligent agents — with an emphasis on secure, production-aware design and
practical projects.
What you'll learn
Understand core concepts of neural networks (perceptron, MLP, CNN, RNN) and when to apply each.
Preprocess and prepare datasets for training: cleaning, normalization, augmentation, and train/validation splitting.
Build, train, and evaluate simple neural models (classification/regression) using common frameworks (e.g., PyTorch / TensorFlow).
Design and implement basic intelligent agents (rule-based and RL-style agents) that interact with constrained environments.
Identify common AI security threats (data poisoning, model inversion, adversarial examples) and apply basic mitigation strategies.
Deliver a small capstone: a working demo that links a model + agent + basic security checks, accompanied by documentation and test results.
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