Sorry, this entry is only available in Japanese. For the sake of viewer convenience, the content is shown below in the alternative language. You may click the link to switch the active language.

Frontier Artificial Intelligence II 2017(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. Applied Deep Learning 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).

  • 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
  • It is an independent course and thus a credit from the university won’t be given.(Notes:The number of participants will be limited to about 50 people due to the limitation of the exercise system.

Q & A

Program syllabus

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


date title contents
1. 2017/09/26 Introduction/Guidance – Summarization of the spring lecture
– Introduction of the autumn lecture, this year’s highlights,last year state, assessment of achievement, etc…
2. 2017/10/03 Advanced image recognition1 Object recognition, CNN, preprocessing, data augmentation, Transfer Learning, Fine-Tuning,Reuse of pre-trained network
3. 2017/10/10 Reinforcement1 Outline of reinforcement learning
– problem establishment in reinforcement learning
– value function based approach and policy based approach
– Introduction of Chainer, ChainerRL
4. 2017/10/17 Generative models1 Outline of generative models, RBM
5. 2017/10/24 Big data and Deep Learning HPC, GPU, Profilers, Database and Deep Learning
6. 2017/10/31 Team development(methodology, teaming) Team development, Git and workflow, the project, precautions, etc…
7. 2017/11/07 Advanced image recognition2 Region proposal, Semantic segmentation, Fast/Faster RCNN, Deconvolution, FCN
※2017/11/14 (no lectures)
8. 2017/11/21 Reinforcement learning2 Value function based approach
– Case studies (Acrobot, Atari, etc…)
9. 2017/11/28 Generative models2  Variational Auto Encoder (VAE) and respective topics
10. 2017/12/05 Advanced image recognition3  TBA
11. 2017/12/12 Generative models3 Generative Adversarial Networks (GAN) and respective topics
12. 2017/12/19 Reinforcement learning3 Policy based approach
13. 2018/1/9 Interim report Lightning talk style
2018/1/16 a supplementary class
2018/1/20 Final report (Deep Learning Day) Keynote address, presentation, poster and demo session