Course:
Course Program:
Topics: Convolutional neural networks. autoencoders, their sparse, denoising variants, and their training. Regularization methods for preventing overfitting. Stacked autoencoders and end-to-end networks. Recurrent and recursive networks. Multimodal approaches. Deep architectures for vision, speech, natural language processing, and reinforcement learning.
Prerequisite: This is an advanced course on machine learning so you should have a good knowledge of the essential machine learning algorithms (Cmpe 544 and 545, or equivalent).
Textbook:
I Goodfellow, Y Bengio, A Courville (2016). Deep Learning, The MIT Press. (See www.deeplearningbook.org) and additional articles when necessary.
Grading:
- %40 Weekly quizzes and In-class presentation
- %40 Project
- %20 Final exam