Developers and Managers with a programming background interested in Machine Learning for Data Analysis
This course covers modern artificial intelligence algorithms based on deep neural networks. It starts with a review of the necessary computer vision, neural networks and statistics background, and then provides an in depth coverage of the different deep learning architectures such as deep convolution networks, sparse autoencoders, recurrent neural networks, belief networks and reinforcement learning techniques. Programming projects on the different deep networks through state of the art libraries using Theano, Google’s Tensor Flow, and Microsoft’s CNTK are described. Applications of deep learning to computer vision, text classification, speech recognition and optimization problems are presented. Some recent research papers in this field will also be explained in the course.
Upon successful completion of this course, the student will be able to:
- Understand the mathematical and statistical background in Machine Learning
- Understand the fundamental concepts in deep neural networks
- Program the different deep architectures using the modern ML libraries
- Understand the state of the art in ML and the future directions in Artificial Intelligence
Good programming knowledge of Python, Java or C# and a prior course in Machine Learning.