Institute of Science and Technology · Computer Science and Engineering (%30 English) · Master
ECTS: 7.5 T+P+L: 3+0+0 Departmental Elective
Coordinator:
Course Objective
The mani objective of this course is to learning the deep models to solve real world problems with efficiency.
Course Content
This course serves as starting point for the cutting-edge demanding field of Artificial Intelligence (AI). Deep learning is a branch of AI that focuses on developing to make accurate decisions. The course will help to develop both theoretical and practical foundations of deep learning. At the end of the course students will be able to build own systems that employee deep neural networks to solve a problem.
Course Learning Outcomes
- Develop a basic understanding of machine learning concepts
- Understand neural networks
- Dive into deep learning
- Implement deep learning projects with tensorflow, keras, caffee etc
- Terminologies of deep learning
- Regularization, hyperparameter training of deep networks
- Convolutional neural networks
- Representation Learning
- Reinforcement learning concepts and implementation
- Generative models and their applications