Singapore University of Social Sciences

Artificial Intelligence, Machine Learning, and Deep Learning in Finance 金融领域的人工智能、机器学习和深度学习

Artificial Intelligence, Machine Learning, and Deep Learning in Finance 金融领域的人工智能、机器学习和深度学习 (FTH521)

Applications Open: To be confirmed

Applications Close: To be confirmed

Next Available Intake: To be confirmed

Course Types: To be confirmed

Language: Chinese

Duration: 6 months

Fees: To be confirmed

Area of Interest: To be confirmed

Schemes: To be confirmed

Funding: To be confirmed

School/Department: School of Business


Synopsis

FTH521 Artificial Intelligence, Machine Learning, and Deep Learning in Finance constructs a framework to explore and master the intricate mechanisms underpinning data analysis, predictive modelling, and financial process automation. This curriculum delves into a comprehensive array of sophisticated techniques, algorithms, and models that demystify the complexities of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). It encompasses an in-depth study of advanced supervised and unsupervised ML models, the architecture and functionality of artificial neural networks (ANN), convolutional neural networks (CNN), long short-term memory networks (LSTM), generative adversarial networks (GANs), and cutting-edge natural language processing (NLP) techniques. By harnessing and synthesizing these technologies, students are empowered to extract pivotal insights, augment operational efficiency, and formulate more precise, data-driven strategies to catalyse financial success. FTH521 金融中的人工智能、机器学习和深 度学习致力于构建包括数据分析、预测建模和金融流程自动化的框架体系,深入浅出地探 讨有关人工智能(AI)、机器学习(ML) 和深度学习(DL) 的一系列技术、算法和模型, 涵盖 了对高级监督和无监督机器学习模型、人工神经网络(ANN)、卷积神经网络(CNN)、长短 期记忆网络(LSTM)、生成对抗网络(GAN) 的架构和功能的深入研究,以及最前沿的自然 语言处理(NLP)技术。本课程旨在帮助学生了解和整合这些技术,培养从数据中挖掘关 键信息、提高运营效率,并制定更精确的数据驱动策略以促进金融成功的能力。

Level: 5
Credit Units: 5
Presentation Pattern: EVERY JULY

Topics

  • Fundamentals of AI and Machine Learning in Finance 金融领域人工智能和机器学习的基础 知识
  • Advanced Supervised Learning Techniques for Financial Modelling 金融建模的高级监督学习技术
  • Unsupervised Learning and Its Applications in Finance 无监督学习及其在金融中的应用
  • Designing Artificial Neural Networks for Financial Analysis 为金融分析设计人工神经网络
  • Convolutional Neural Networks in Financial Data Processing 金融数据处理中的卷积神经网络
  • Long Short-Term Memory Networks for Time-Series Analysis in Finance 用于金融时间序列分析的长短期记忆网络
  • Utilizing Generative Adversarial Networks in Financial Strategies 在金融策略中利用生成对抗网络
  • Natural Language Processing for Financial Market Insights 应用于金融市场的自然语言处理
  • Predictive Modelling and Data-Driven Financial Decision Making 预测建模与数据驱动的金融决策
  • Operational Efficiency Enhancement through AI and ML 通过人工智能和机器学习提升运 营效率
  • Ethical Considerations and Challenges in AI for Finance 金融人工智能的道德考虑和挑战
  • Emerging Trends and Future Directions in AI and Finance 人工智能和金融的新兴趋势和未来方向

Learning Outcome

  • Select strategies using generative adversarial networks (GANs) and natural language processing (NLP) to enhance financial decision-making 选择使用生成对抗网络(GAN) 和自然语言处理(NLP) 的策略,以增强金融决策能力
  • Critique the effectiveness of various deep learning models in extracting insights and augmenting operational efficiency in finance 评估各种深度学习模型在挖掘关键信息和 增强金融运营效率方面的有效性
  • Appraise the impact of AI and machine learning technologies on the future landscape of the financial industry 评估人工智能和机器学习技术对未来金融行业格局的影响
  • Value the application of AI, ML, and DL techniques in financial data analysis and predictive modelling 评估人工智能、机器学习和深度学习技术在金融数据分析和预 测建模中的应用
  • Construct models using advanced supervised and unsupervised machine learning algorithms 使用高级监督和无监督机器学习算法构建模型
  • Design and implement artificial neural networks, including CNNs and LSTMs, for financial process automation 设计和实施卷积神经网络(CNN)和长短期记忆网络 (LSTM)等人工神经网络用于金融流程自动化
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