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Deep Learning, Spring 2014
This is a graduate course on deep learning, one of the hottest topics in machine learning and AI at the moment.
In the last two or three years, Deep learning has revolutionized speech recognition and image recognition. Deep learning is widely deployed by such companies as Google, Facebook, Microsoft, IBM, Baidu, Apple and others for audio/speech, image, video, and natural language processing.
Recommended textbooks, on-line material, links
The course covers a wide variety of topics in deep learning, feature learning and neural computation. It covers the mathematical methods and theoretical aspects as well as algorithmic and practical issues. Deep Learning is at the core of many recent advances in AI, particularly in audio, image, video, and language analysis and undestanding.
Who Can Take This Course?
This course is primarily designed for student in the Data Science programs. But any student who is familiar with the basics of machine learning can take this course.
The only formal pre-requisites is to have successfully completed “Intro to Data Science” or any basic course on machine learning. Familiarity with computer programming is assumed. The course relies heavily on such mathematical tools as linear algebra, probability and statistics, multi-variate calculus, and function optimization. The basic mathematical concepts will be introduced when needed, but students will be expected to assimilate a non-trivial amount of mathematical concepts in a fairly short time.
Familiarity with basic ML/stats concepts such as multinomial linear regression, logistic regression, K-means clustering, Principal Components Analysis, and simple regularization is assumed.
The topics studied in the course include: