|In this semester, we will cover two different topics: IoT and deep learning. The first part of this lecture will focus on wireless adhoc network technology that provide low energy and low latency lightweight communication for WSN (wireless sensor networks) and MANET (mobile adhoc networks). The second part of this class will focus on deep learning techniques of artificial intelligence. Recently, deep learning techniques are used not only for machine learning but also applied to other fields of information technology as well as to the other fields of engineering and science in general. We will discuss the basic concepts of deep learning such as CNN and RNN by studying the classical papers of machine learning and deep learning. Then, we move to more recent topics of deep learning such as reinforcement learning (RL) and generative adversarial networks (GAN) by reviewing the recent research papers in deep learning.|
1. Lecture Note 1 was updated on August 30th.
2. Lecture Note 1 was updated again on September 5th.
3. Lecture Note 2 was updated on September 7th.
4. Lecture Note 2 was updated again on September 12th.
5. Lecture Note 2 was updated again on September 13th.
6. Lecture Note 1 was updated again on September 13th.
7. Lecture Note 4 was updated on September 26th.
8. Lecture Note 5 was updated on October 10th.
9. Lecture Note 6 and 7 were updated on October 17th.
10. 기말고사 시험범위는 CH6. WSN Routing Protocol 부터 7. MANET 그리고 Indoor Localization, Deep Learning 까지 입니다.
▣ Lecture slide
1. IoT and WSN
2. WLAN WPAN
Paper List : Paper List.docx
Convolutional Neural Network (12 papers)
Natural Language Processing / Recurrent Neural Network (12 papers)
Optimization / Training Techniques (10 papers)
Uderstanding / Generalization / Transfer (7 papers)
Recent papers (12 papers)
Classic papers (10 papers)
▣ Reading List