NTU Course
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Financial Technology

Offered in 112-2
  • Serial Number

    37230

  • Course Number

    EE5183

  • Course Identifier

    921 U2610

  • No Class

  • 3 Credits
  • Elective

    GRADUATE INSTITUTE OF ELECTRICAL ENGINEERING / GRADUATE INSTITUTE OF COMMUNICATION ENGINEERING / GRADUATE INSTITUTE OF BIOMEDICAL ELECTRONICS AND BIOINFORNATICS

      Elective
    • GRADUATE INSTITUTE OF ELECTRICAL ENGINEERING

    • GRADUATE INSTITUTE OF COMMUNICATION ENGINEERING

    • GRADUATE INSTITUTE OF BIOMEDICAL ELECTRONICS AND BIOINFORNATICS

  • CHE LIN
  • Tue 7, 8, 9
  • 電二146

  • Type 3

  • 60 Student Quota

    NTU 60

  • No Specialization Program

  • English
  • NTU COOL
  • Core Capabilities and Curriculum Planning
  • Notes
    The course is conducted in English。
  • NTU Enrollment Status

    Enrolled
    0/60
    Other Depts
    0/0
    Remaining
    0
    Registered
    0
  • Course Description
    - Financial technology (Fintech) is a broad category that refers to the innovative use of technology in the design and delivery of financial services and products. - While many technology innovations play important parts in revolutionizing financial services, this course focuses on deep learning (DL) and its applications in FinTech. - DL is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts with a deep representation of many layers. - In this course, we hope to demonstrate how DL can be applied to achieve superior predictive performance in FinTech applications.
  • Course Objective
    In this course, we will first provide an overview of how deep learning revolutionizes the financial industry. We will then provide basics for machine learning (ML) and DL. Finally, we will provide several case studies on how to apply ML/DL to solve real-world FinTech problems. Students are expected to learn how to apply ML/DL algorithms in FinTech applications via completing programming homework and final project.
  • Course Requirement
    Machine learning - Capable of using Python packages (e.g., numpy, pandas, scikit-learn) to process data - Capable of using Pytorch to construct/test regular deep models - Capable of understanding and modifying the source code of advanced models Math - Calculus; Linear Algebra
  • Expected weekly study hours before and/or after class
  • Office Hour
    Prof. Che Lin: Wed. 14:30~16:30 online/by appointment TAs: Name: Ming-Yi Hong (洪明邑) Email: d09948005@ntu.edu.tw Name: Miao-Chen Chiang (江妙真) Email: d09948004@ntu.edu.tw TA hour: Thur. 14:30~15:30 online (https://meet.google.com/fop-yvak-vxp, please inform the TAs via email in advance)
    *This office hour requires an appointment
  • Designated Reading
  • References
    1. Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville 2. Advances in Financial Machine Learning by Lopez de Prado, MarcosDesignated Reading
  • Grading
    5%

    Expert talk reports (x3)

    2% for each

    15%

    Midterm: Proposal Presentation

    15%

    Paper Presentation

    30%

    Programming Homework (x3)

    Any changes will be notified in the first class!

    35%

    Final Project

    Group presentation: 25% Individual report: 10%

  • Adjustment methods for students
  • Make-up Class Information
  • Course Schedule
    2/20Week 11. Course announcement 2. Video record: Introduction to FinTech / How Deep Learning is related to FinTech (+ group discussion)
    2/27Week 2Pre-class: 1. Machine learning basics 2. DFN basics In-class: Group discussion (pre-class materials)
    3/05Week 3Case Study: Bank direct marketing
    3/12Week 4Recurrent Neural Networks
    3/19Week 5Case Study: AsiaYo / Stock prediction
    3/26Week 6Transformer and Bidirectional Encoder Representations from Transformers (BERT)
    4/02Week 7Case Study: Style4Rec / Push4Rec
    4/09Week 8Midterm: Proposal Presentation
    4/16Week 9Expert talk
    4/23Week 10* Graph Neural Networks
    4/30Week 11* Case Study: SynHIN / TreeXGNN
    5/07Week 12* Expert talk
    5/14Week 13Paper Presentation
    5/21Week 14* Case Study: THeGAE / RAG
    5/28Week 15Expert talk
    6/04Week 16Final Presentations