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

li

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%