Machine Learning
Ethics Content Description
This ethics lecture introduces students to machine learning (ML) as a socio-technical phenomenon, surveys key ethical issues particularly relevant to ML practitioners, explores the complexity of ethical reasoning, and emphasizes the importance of reflective, ethically informed programming practices.
Course Description
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.