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What is Data-Driven Model Learning Techniques about?

The lecture covers the integration of data, models, and learning in machine learning systems, emphasizing the importance of empirical risk minimization and probabilistic modeling. It discusses various learning algorithms, including maximum likelihood estimation (MLE) and maximum a posteriori (MAP), as well as the challenges of overfitting and the role of regularization. Additionally, it explores latent-variable models and their significance in understanding generative processes and improving model interpret

Author

huynhnhuttancm

Language

EN