Frontier Artificial Intelligence 2017
We provide an advanced programme ; starting from basics of MLP and neural network, designed to get
you learning core technologies of Deep Learning and the latest topics step by step. Based on the policy of “Practice makes perfect”, we aim to get you acquire the technique through exercises. 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 221, The University of Tokyo
- Target：Graduate students of the University of Tokyo
●Program syllabus
（The contents are being adjusted, may be changed.）
| date | title | contents | |
|---|---|---|---|
| 1 | 2017/4/11 | Introduction | About Deep Learning/a history of AI technology, impacts on society/perspective of the lecture and precautions/ lecturers’ introduction |
| 2 | 2017/4/18 | Machine Learning 1 | Python and Linear Algebra，matrix・tensor，bridge of mathematical expression and implementation |
| 3 | 2017/4/25 | Machine Learning 2 | k-NN, Logistic Regression, Softmax, train/dev/test dataset, learning process |
| 4 | 2017/5/2 | Perceptron + Feed Forward Network, Gradient Descent | Gradient Descent, MLP, Logistic Regression, Softmax |
| 5 | 2017/5/9 | Gradient Descent, Stochastic Gradient Descent, Optimizers | TensorFlow basics、various optimizers、initializing the weight etc… |
| 6 | 2017/5/16 | Autoencoders | Deep Learning、Feature Extraction、Outline of Autoencoder、dA, SdA, Sparse Coding, GPU |
| 7 | 2017/5/23 | Convolutional Neural Networks(CNN) | Basic CNN, Convolution, Pooling |
| 8 | 2017/6/6 | Convolutional Neural Networks(CNN) 2 | Image Processing, Data Handling, Data Augmentation、Batch Normalization, visualization, the latest trends |
| 9 | 2017/6/13 | RNN Basics | Sequence data, RNN |
| 10 | 2017/6/20 | RNN and NLP | Embedding, Projection, Word2vec, sequence-to-sequence |
| 11 | 2017/6/27 | RNN, NLP, Image Processing | Attention, Image caption |
| 12 | 2017/7/4 | Summary and Advanced Topics | Advanced Topics |