Syllabus
Introduction to Data Mining and Machine Learning, Classical Machine learning paradigms for Data Mining, Fundamentals of Supervised and Unsupervised Machine Learning algorithms, Feature Selection – Methods. Machine learning for anomaly detection using Probabilistic Learning, Unsupervised learning, Combination learners, Evaluation methods, Hybrid detection. Machine learning for network scan detection and Network traffic profiling, Deep Learning – Optimization Techniques – Deep Feedforward Networks, Convolution Networks, Sequence Modeling – Recurrent and Recursive Nets, LSTM, Autoencoders, Deep Reinforcement learning. Representation Learning, Structured Probabilistic Models for Deep Learning, Deep Generative Models – Generative adversarial network and its variants, Applications in malware analysis and anomaly detection- Behavioral Analysis of Advances Malware such as Ransomwares. Applications of Natural language processing(NLP) in Cyber Security, Attacks on Large Language Models(LLM)- Deep fake technology, Generative AI- Uses, Threat, Simulation and Detection.