Deep Learning is a subfield of machine learning mostly concerned with concepts and techniques built on top of the artificial neural network which in turn was inspired by the structure and functionality of human brains. This branch of machine learning is increasingly gaining popularity as deep learning systems are taking over all artificial intelligent tasks, ranging from image classification, language modeling, machine translation, playing games, autonomous vehicle driving, speech recognition, cancer detection and numerous other applications and is behind many recent advances in Artificial Intelligence (AI). In this course you will gain both theoretical and practical knowledge of deep learning concepts and techniques. So, welcome aboard!

Course objectives

By the end of the course you are expected to gain the following skills:

1. Understand fundamentals of artificial neural network, and deep neural networks.
2. Develop an understanding on how to train a neural network.
3. Determine how a deep neural network can be designed, and implemented to solve real world problems.
4. Demonstrate an in-depth understanding of one/more concepts introduced in the deep learning course through a final project.

Prerequisites

For undergraduate students:

1. MATH 3195
2. CSCI 3412

For graduate students:

1. The graduate standing.

Topics covered

1. Introduction to Deep Learning
2. Machine Learning review
3. Feed forward ANN
4. Convolution Neural Networks
5. Recurrent Nueral Network
6. Deep Reinforcement Learning
7. Unsupervised Deep Learning
8. Ethics in AI