一、課程說明 (Course Description)
This class introduces the concepts and techniques of deep learning. The
course consists of three parts. In the first part, we give a quick introduction of
classical machine learning and review some key concepts required to understand
deep learning. In the second part, we
discuss how deep learning differs from classical machine learning and
explain why it is effective in dealing with complex problems such as the image and
natural language processing. Various CNN and RNN models will be covered. In the
third part, we introduce the deep reinforcement learning and its applications.

This course also gives coding labs. We will use Python 3 as the main
programming language throughout the course. Some machine learning libraries such
as Scikit-learn and TensorFlow will be explained and heavily used.

Students are required to have proper understanding of Calculus, Linear
Algebra, Probability Theory, and the Python programming language. Although it
would be helpful, knowledge about classical machine learning is NOT required.

IMPORTANT: Students will group (3~4 people a group). This class requires
**each group of students to prepare a GPU card** to perform the necessary
computing. You can follow this
link (http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning/) to
decide which GPU card to go for. NO GPU CARD PROVIDED IN THE CLASS.

二、指定用書 (Textbook)
Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT Press,
2016, ISBN:
0387848576

四、教學方式 (Teaching Method)
Video Lecture + Labs

五、教學進度 (Syllabus)
1. Fundamentals of Machine learning (5 weeks)
2. Neural Networks (NNs): Design
3. NN Optimization and Regularization
4. Computer Vision & CNNs
5. Natural Language Processing & RNNs
6. Unsupervised learning
7. Semi-supervised & transfer learning
8. Deep reinforcement learning (2 weeks)

六、成績考核 (Evaluation)
Quiz: 20%
Assignments: 40%
Competitions/projects: 40%