DEEP LEARNING AND ITS APPLICATIONS (4 Credits)

Learning Outcomes:

On successful completion of this course, students will be able to: Identify various building blocks of deep learning; Comprehend the importance of deep learning in solving real life problems; Apply appropriate deep learning architectures for various applications; Analyze the architectures and performances of deep learning models; Evaluate the advancements and challenges in deep learning research; Design new approaches that can improve the deep learning performances.

Topics:

1. Getting Started with Deep Learning;
2. Building Blocks of Neural Networks;
3. Diving Deep into Neural Networks;
4. Fundamentals of Machine Learning;
5. Convolutional Neural Networks;
6. Deep Learning with Sequential Data;
7. Text Classification;
8. Generative Networks;
9. Modern Network Architectures in Computer Vision;
10. Modern Network Architectures in Text Analysis;
11. Enrichment Activity: Thesis Consultation Consultation to thesis supervisor;
12. Enrichment Activity: Thesis Consultation Consultation to thesis supervisor;
13. Enrichment Activity: Guest Lecture Deep Learning for Computer Vision;
14. Enrichment Activity: Thesis Consultation Consultation to thesis supervisor;
15. Enrichment Activity: Guest Lecture  A Journey into Sound.