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Lecture 11 Introduction Multimodal Machine Learning Carnegie Mellon University
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=VIq5r7mCAyw
Lecture 1.1: Introduction (Multimodal Machine Learning, Carnegie Mellon University) • Topics: Research and Technical Challenges in Multimodal Machine Learning, Course Syllabus and Requirements • ---------------------------------------------------------------------------------------------------------------- • Carnegie Mellon University 11-777 Multimodal Machine Learning, 2020 Fall • Website: https://cmu-multicomp-lab.github.io/m... • Instructor: Louis-Philippe Morency • 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.
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