Artificial Neural Networks

Artificial Neural Networks are at the forefront of artificial intelligence enabling machines to talk to us, translate languages, color black and white pictures, caption artifacts, and create music. While classical machine learning methods are dependent upon identifiable features in the input data, artificial neural networks build complex concepts same way the human brain processes large but simpler stimuli to classify, recognize, analyze and synthesize. Artificial Neural Networks are also called deep learning systems due to the quintessential layering of the network from simple to complex concepts. Artificial Neural Networks are capable of utilizing a wide range of data sets including unstructured data such as text, speech, images, audio and video. Talking products from Apple, Amazon, Microsoft, and Google all use artificial neural networks as do Tesla’s self-driving cars. Artificial neural networks are also increasingly being used in NLU (Natural Language Understanding). In this course, students will learn applications of artificial neural networks for solving artificial intelligence tasks. Students will explore design, architecture, and applications of networks for practical applications. Student will learn how artificial neural networks such as multilayered perceptron are implemented in Python. Student will also learn popular tool sets including TensorFlow and Keras and their use in implementing scalable Artificial Intelligence Systems. Note: this course requires work in the Python programming language - I&C SCI X426.59 Intermediate Python is a required prerequisite.