人工智慧總整與實作

This course is the last one of a series of artificial intelligence related undergraduate courses. As a capstone course, there will be substantial hands-on work and the students are expected to gain experiences from implementation and investigation themselves. Students are expected to already possess basic knowledge of AI, such as the topics covered in previous courses in this series (Introduction to AI, Introduction to ML). Such material will only be quickly reviewed without much in-class discussion. The lectures will mainly cover more advanced or specialized topics; examples include: game AIs, affective computing, reinforcement learning, unsupervised learning, information retrieval, natural language processing, logical inference engine, evolutionary computing, swarm intelligence, self-supervised learning, semi-supervised learning, generative models, etc.

Previous knowledge / course in introductory artificial intelligence (AI) and/or machine learning (ML) Data structure; linear algebra, probability Programming in a high-level language

We will use New E3 for class material distribution and assignment submission. The instruction and class material are all in English, but you can use Chinese or English for the exam and reports.

Individual projects x2 (40%) Exam x1 (15%) Final (group) project (40%) Short commentary essay (5%)

Lecture notes

地點

EC709

時間

By appointment

聯絡方式

e-mail: wangts@cs.nycu.edu.tw 56689