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Frontier Artificial Intelligence II 2016(The course of Graduate School of The University of Tokyo)

The emergence of Deep Learning technology has dramatically improved AI technology, and had a big impact on diverse industry and social institution.  In the near future, every industry and its types should be influenced by AI technology.  Frontier artificial intelligence II provides a project-style lecture intended  acquisition of more practical ability of research and development for the learners who have a basic understanding of Deep Learning and can construct the models.  Based on the idea “Practice makes perfect”, we aim to let you acquire the technique through the exercise.  In exercise, you can concentrate on the point of the lecture by using our specific development platform “ilect.net” : enables you to code in Python with GPU on a browser(The detail of iLect is here).

  • Date:Every Tuesday  fourth class(from 2:55 PM to 4:40 PM)
  • Place:Faculty of Engineering Building 2, Second floor lecture room 223, The University of Tokyo
  • Target:Graduate students of the University of Tokyo(Notes:The number of participants will be limited to about 50 people due to the limitation of the exercise system.)


Program syllabus

(The contents are being adjusted, may be changed.)


date title contents
1. 2016/10/ 4 Introduction/Guidance Summarization of the spring semester and Introduction of the autumn semester class, Team development and notation(outline), perspective of this lecture and precautions etc…
2. 2016/10/11 Advanced image recognition Reuse of pre-trained network, Transfer learning, Fine-Tuning, VGG, an introduction to Caffe
3. 2016/10/18 Deep Learning and Big Data HPC, GPU, Profilers, Database and Deep Learning
4. 2016/10/25 Reinforcement learning(DQN) Reinforcement learning, Policy and Value function,Q Learning, DQN
5. 2016/11/ 1 Team development and project Team development, Git and workflow, project management
6. 2016/11/15 Team introduction Overview of the team project, Team introduction
7. 2016/11/22 Advanced image recognition 2 Region proposal, Semantic segmentation, Fast/Faster RCNN, Deconvolution, FCN
※2016/11/29 (no lectures)
8. 2016/12/ 6 Reinforcement learning(DQN)2 Advanced topic, Reinforcement Learning, A3C
9. 2016/12/13 Web and Deep Learning Knowledge Representation, DeepWalk, Knowledge Graph
10. 2016/12/20 Developing time
11. 2017/ 1/10(The date has been changed) Interim report
12. 2017/ 1/21 Final report