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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.
application
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 – TBA – 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 |