本课程涵盖数据分析与数据挖掘、机器学习、统计分析、神 经网络、深度学习、量子计算与量子机器学习等细分内容； 面向于研究生，博士生以及国际留学生；基于离散数学、数 值分析、统计学、量子力学、软件工程、计算机科学等多基 础学科。除了使学生掌握数据分析中业务理解、数据收集、 数据预处理、数据呈现的基本方法，以及数据挖掘中常用的 预测模型（回归、逻辑、时间序列）、聚类算法、关联规则 发现和分类、偏差侦测等方法之外。更重要的是培养学生面 对AI行业数据的时候，分析问题和解决问题的能力。全方位 使学生从理论高度理解人工智能核心算法的设计、优化与应用，同时结合众多实际案例的分享，让学生学以致用，为学生毕业后从事本专业范围内的各项工作奠定坚实的理论基础。

This course covers data analysis and data mining (DADM), machine learning (ML), statistical analysis, neural networks (NN), deep learning (DL), quantum computing and quantum machine learning (QML), and so on. It’s suitable for graduate students, doctoral students and international students. The required basic courses include discrete mathematics, numerical analysis, statistics, quantum mechanics, software engineering, computer science and other basic disciplines. In addition to the basic methods of business understanding, data collection, data preprocessing and data presentation in data analysis, as well as the commonly used prediction models (regression, logic, time series), clustering algorithms, association rule discovery and classification, deviation detection and other methods in data mining. What's more important is to cultivate students' ability to analyze and solve problems when facing AI industry data. It enables students to fully understand the design, optimization and application of the core algorithm of artificial intelligence (AI) from a theoretical perspective. At the same time, combined with the sharing of numerous practical cases, it enables students to put what they learn into practice, laying a solid theoretical foundation for students to engage in various works within the scope of their major after graduation.