Lecture 1.2: Datasets (Multimodal Machine Learning, Carnegie Mellon University) скачать в хорошем качестве

Lecture 1.2: Datasets (Multimodal Machine Learning, Carnegie Mellon University)

Lecture 1.2: Datasets (Multimodal Machine Learning, Carnegie Mellon University) Topics: Multimodal applications and datasets; research tasks and team projects ---------------------------------------------------------------------------------------------------------------- Carnegie Mellon University 11-777 Multimodal Machine Learning, 2020 Fall Website: https://cmu-multicomp-lab.github.io/m... Instructor: Paul Liang Multimodal machine learning (MMML) is a vibrant multi-disciplinary research field which studies computational approaches for modeling heterogenous data from multiple modalities. The course presents fundamental mathematical concepts in machine learning and deep learning relevant to the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation & mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. The course also discusses recent state-of-the-art models and applications of multimodal machine learning.