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.