Ethics and Fairness in Machine Learning

Related lectures (31)

Page 1 of 4

Covers the fundamentals of deep learning, including data, architecture, and ethical considerations in model deployment.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Introduces the Critical Data Studies course, covering data models, trust, reproducibility, biases, and ethics.

Delves into the impact of bias in machine learning models and the importance of evaluating potential harm in developing such systems.

Explores the Ethics and Law of AI, focusing on data ethics, bias, and social justice in AI systems.

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Provides an overview of deep learning concepts, focusing on data, model architecture, and challenges in handling large datasets.

Discusses the ethical implications of NLP systems, focusing on biases, toxicity, and privacy concerns in language models.

Explores models, learning paradigms, and applications in Mathematics of Data.

Explores the evolution from data analysis to AI and ML, emphasizing big data, machine learning, and social media interaction.