Advanced AI: Techniques, Applications, and Ethics

Most AI practitioners can run a model. Fewer can select the right one for the problem at hand, trace the causal story behind their data, and design systems that genuinely empower the people they affect. This course closes that gap, delivering the technical depth and ethical judgment that separate thoughtful AI expertise from surface-level familiarity.

You'll classify machine learning types and apply XGBoost and CNNs to regression and classification tasks, running working Python code throughout. You'll build causal models using Bayesian networks and the DoWhy framework, integrate knowledge graphs for structured reasoning, and generate language and analyze sentiment with transformer models including GPT-2 and BERT. Then you'll program competitive AI agents using minimax algorithms and cooperative swarms with particle optimization before applying a rigorous ethics arc covering bias mitigation, privacy trade-offs, impossibility theorems, Value-Sensitive Design, and the Capability Approach.

By the end of this course, you'll be able to select, build, and ethically evaluate AI systems across a range of real-world domains, equipped with both the technical skills and the principled design frameworks to ensure your work genuinely enhances human capability.