This course aims to teach learners about the history and evolution of artificial intelligence, deep learning, and its applications in speech recognition. By the end of the course, students will be able to understand the fundamental concepts of deep learning, neural networks, and their role in speech recognition tasks. The course covers topics such as perceptron learning, loss functions, stochastic gradient descent, multi-layer perceptron, language models, acoustic models, and deep belief networks. The teaching method includes lectures on historical milestones, theoretical frameworks, and practical applications in speech recognition. This course is intended for individuals interested in artificial intelligence, deep learning, speech recognition, and machine learning across various platforms.
Overview
Syllabus
Introduction.
Birth of Artificial Intelligence.
Checkers (Arthur Samuel, 1956).
ELIZA (Weizenbaum 1966).
2001 Space Odyssey (Stanley Kubrick, 1968).
Deep Blue (IBM, 1997).
Deep Learning (Hinton, 2006).
Jeopardy (IBM, 2011).
The imitation game (2014).
Improve on Task T with respect to performance metric P based on experience E.
Perceptron Learning (Rosenblatt, 1957).
A probabilistic framework.
Loss function Loss function between two probability distributions.
Stochastic gradient descent.
N-ary classification.
Multi-layer Perceptron (Werbos, 1974).
Binary Classification Tasks.
Fundamental Equation of Speech Recognition.
Language Model.
Acoustic Model (Hidden Markov Models) HUT.
Neural Networks for Speech Recognition in the 1990s.
Neural Network Winter for Speech Recogntion.
Open Challenge Tasks (DARPA).
Deep Belief Networks = Deep Neural Networks.
Deep Learning for Speech (Deng et al., 2010).
Deep Neural Networks: What was new?.
DNN on Face Images (2012) Deep Belief Net on Face Images.
Deep Learning in Speech Recognition.
Machine Learning across Apple Products.
Siri Architecture.
Hands-Free Siri.
Dictation.
Voicemail transcription.
Taught by
Stanford Online
