數據所(Institute of Data Science)

Artificial intelligence (deep learning) has achieved great success in many fields. For example, image recognition, medical image analysis, and self-driving cars. Each application has a specific neural network model and a learning strategy in the field of deep learning. This course will explore the deep learning architecture details and use computer vision-related applications as examples to introduce learning strategies. This course contains not only these essential expositions of deep Learning but also many practical programming tasks. The goal is to give students the ability to build a corresponding deep neural network based on their problems.
Artificial intelligence (deep learning) has achieved great success in many fields. For example, image recognition, medical image analysis, and self-driving cars. Each application has a specific neural network model and a learning strategy in the field of deep learning. This course will explore the deep learning architecture details and use computer vision-related applications as examples to introduce learning strategies. This course contains not only these essential expositions of deep Learning but also many practical programming tasks. The goal is to give students the ability to build a corresponding deep neural network based on their problems.

週次 Week	進度說明 Progress Description
1	Course Introduction
2	Machine learning: The data-driven approach
3	Loss Functions and Optimization
4	Introduction to Neural Networks / Multilayer Perceptron
5	Convolutional Neural Networks
6	Intro to Pytorch and Tensorflow under Ubuntu System
7	CNN Architectures: AlexNet, VGG / Final Project Proposal due
8	Training Neural Networks
9	In-class midterm
10	CNN Architectures: GoogLeNet, ResNet, DenseNet, EfficientNet, etc
11	Proposal presentation
12	Object Detection
13	Semantic Segmentation
14	Unsupervised Learning: Generative Models
15	Recurrent Neural Networks
16	Semi-Supervised learning
17	Invited talk
18	Final Project Presentation