This is a required course in the Machine and Deep Learning specialized study program
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.
Note: this course requires work in the Python programming language - I&C SCI X426.59 Intermediate Python is a required prerequisite.
Rashed Iqbal, Ph. D is a Program Manager, Data Solutions at Teledyne Technologies in California. He has two areas of interest and expertise: Data Science and Machine Learning, and Transitioning Traditional Organization to Agile and Lean Methods. He practices, consults, and teaches in these domains. . He is part-time Adjunct Professor in Economics Department at UCLA where he teaches graduate courses in Data Science. Rashed also teaches at UCLA Extension and at UCI Continuing Education. Rashed undertook multiple entrepreneurial ventures in these areas. His current area of research is Narrative Economics that studies impact of the popular narratives and stories on economic fluctuations. He is using NLP and Deep Learning to extract narratives in human communication. He believes narrative extraction will revolutionize process of human communication to which Narrative Economics is just one of the applications. Rashed has a Ph.D. in Systems Engineering with focus on Stochastic and Predictive Systems and holds current CSM, CSP, PMI-ACP, and PMP certifications. He is also a Senior Member of the IEEE.
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No textbook information is available for this course.
|Event||Date||Day||Start Time||End Time||Location||Room
|START||01/24/2022||Monday||---||---||Online (Access Begins)||---
|END||03/13/2022||Sunday||---||---||Online (Access Ends)||---