Lesson Description

In this course, you will learn about the concepts of Artificial Neural Networks (ANNs), specifically advanced topics on ANN models, understand how to build different types of neural networks, and learn how to lead successful machine learning projects. This course introduces you to ANNs: the bio-inspired models to building artificial intelligence algorithms. ANNs and Deep learning models are the machine learning techniques behind the most exciting capabilities in diverse areas like natural language processing, image recognition, speech recognition, robotics, etc. In this course, we cover the basic components of ANNs, what it means, how it works, and develop code necessary to build various algorithms such as MLPs, SVMs, recurrent neural networks, and also deep convolutional networks.

Weekly Schedule

Mondays 8-10, Location: Learning Management System (LMS)

 

Files

Files Name Presentations Home works Quizzes Midterm Final Exam Related Documents
Chapter 1 Download Download Download Download Download Download
Chapter 2 Download Download Download Download Download Download
Chapter 3 Download Download Download Download Download Download
Chapter 4 Download Download Download Download Download Download
Chapter 5 Download Download Download Download Download Download
Chapter 5 Download Download Download Download Download Download
Chapter 6 Download Download Download Download Download Download
Chapter 7 Download Download Download Download Download Download
Chapter 8 Download Download Download Download Download Download

Course References



1. M. Bishop, "Neural Network for Pattern Recognition", Oxford University Press, 1995.

2. S. S. Haykin, "Neural Network: A Comprehensive Foundation, Processing". CRC Press, 2010.

3. M. Bishop, "Pattern Recognition and Machine Learning", 2006.

4. L. Deng, Y.Dong, "Deep Learning: Methods and Applications.", 2014

Current Teacher Assistans

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Former Teacher Assistans

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Contact

Location:

HerzarJarib  Street, Azadi Square, University of Isfahan, Isfahan, Iran

Call:

+98 31 3793 5638

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