機器學習導論

Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, search engines, genomics, automated medical diagnosis, image recognition, and social network analysis, among many others. This course will introduce the fundamental concepts and algorithms that enable computers to learn from experience, with an emphasis on their practical application to real problems. This course will introduce supervised learning (decision trees, logistic regression, support vector machines, Bayesian methods, neural networks and deep learning), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning. Additionally, the course will discuss evaluation methodology and recent applications of machine learning. Relationship to “Machine learning” Introduction to Machine Learning (this course!) is a new introductory-level course in machine learning (ML) with an emphasis on applying ML techniques. This course is intended for students who are interested in the practical application of existing machine learning methods to real problems, rather than in the statistical foundations and theory of ML covered in the graduate course. Machine Learning is a more mathematically rigorous course in statistical machine learning that provides the background necessary to design and use new ML algorithms. Relationship to "deep learning for autonomous driving” “Deep learning for autonomous driving” focuses on applications of deep learning on autonomous driving. It will cover broad and deep knowledge about deep learning background and implementation issues.

Linear algebra, probability and statistics, programming

課堂講授與上機實作 教材於e3new.nctu.edu.tw

評分: 無期中考,期末考一次 上機作業: machine learning 25%,deep learning 25%,final project 20% 期末考: 25% 課堂參與出席率: 5%

• 機器學習與應用 (Machine learning and its application on image recognition)
• 深度學習與應用Deep learning and its application

以上課講義為主

地點

鄭文皇ED403 張添烜ED406

時間

請e-mail 洽各授課老師

聯絡方式

鄭文皇 whcheng@nctu.edu.tw 張添烜 tschang@g2.nctu.edu.tw