| This course provides the basic knowledge of artificial intelligence and related mathematics. To be general and detailed, the topics include searching, heurisitc searching, simulated annealing, gradient method, computational intelligence, decision trees, naive bayes, k nearest neighorhood, linear models, support vector machine, deep learning components. Given the students are without math foundation, we provide the prerequities in the first class, while in the final class, the students shall finish one project and present this project in class. | |||||||||||||
| This course presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning | |||||||||||||
Methods and Applications of Deep Learning for Artificial intelligence department. Fourth semester second year.
Applied Machine learning course for Artificial Intelligence department. Third semester, 2nd year.
This course covers advanced and latest research issues on Natural Language Processing, including Sentiment Analysis, Topic Modeling, Text Clustering, Text Classification, Information Extraction, Dialogue System, Question Answering, Summarization, Text Generation, Knowledge Graph, Machine Translation. The discussion of these advanced topics will include some of the latest pre-trained models such as: BERT, Hugging Face, GPT, ChatGPT, etc.
The course aims to enable students to acquire sufficient understanding and knowledge about sensors. The fundamentals of sensors, applications of modern sensors will be introduced in this course.