This course extends machine learning into advanced representation, recommendation, temporal, probabilistic, causal, and sequential-decision systems. Learners apply clustering, dimensionality reduction, anomaly detection, recommender systems, ranking, time-series forecasting, causal reasoning, probabilistic modelling, and reinforcement-learning foundations to complex applied problems. The reinforcement-learning and decision-theoretic foundations developed here provide the basis for the alignment techniques covered in course TECH 501 Generative AI & LLM Engineering and the agentic decision-making covered in TECH 502 Agentic AI, Multi-Agent Systems and Orchestration.