Topics in Quantitative Finance: Machine Learning for Finance (2017-18 Module 3)

Announcements

• [NEW] Email is the preferred method of communication. Mail list will be set up soon.

Course Note

• To be uploaded soon

Lectures:

• 01 (02.26 Mon): Course overview (Syllabus), Python, Github, Etc.

Homeworks:

• Set 1 [due by course cancel deadline]:

  • Register on Github.com and let TA know your ID. Give your full name in your profile.
  • Install Python Aanconda and Github Desktop. Send screenshots to TA

Classes:

• Lectures: Monday & Thursday 8:30 AM – 10:20 AM
• Venue: PHBS Building, Room 229

Instructor: Jaehyuk Choi

• Office: PHBS Building, Room 755
• Phone: 86-755-2603-0568
• Email: [email protected]
• Office Hour: Monday & Thursday 1:30 – 2:30 PM or by appointment

Teaching Assistance: Chenru LIU (刘晨茹)

• Email: [email protected]
• TA Office Hour: Tuesday & Friday 1-2 PM (Room 213/214)

Course overview

The purpose of Topics in Quantitative Finance is to introduce students to recent trends and advanced research topics in quantitative methods of business and finance. This year’s course is dedicated to machine learning (ML) for finance. ML has been one of the hottest technology in software engineering. This course will explore the possibility of applying ML to finance and business. The course will give students the basic ideas and intuition behind the popular ML methods and hands-on experience of using ML software package such as SK-learn and Tensorflow (Google). Each student is required to complete a final course project.

Prerequisites

There is no formal prerequisites. However, undergraduate-level knowledge in probability/statistics and previous experience in programming language is highly recommended.

Textbooks and Reading Materials

• PML (primary textbook): Python Machine Learning by Sebastian Raschka
• ISLR: An Introduction to Statistical Learning (with Applications in R) by James, Witten, Hastie, and Tibshirani
• CML: Coursera Machine Learning taught by Andrew Ng
• DL: Deep Learning by Goodfellow, Bengio, and Courville

Assessment / Grading Details

• Attendance 20%, Mid-term Exam 20% (New), Assignments 20%, Final Exam 40%
• Mid-term exam and project proposal will be in week 6 or 7
• Exams are open-book without computer/phone/calculator use
• You may form a group of up to 2 people for course project. Extra credit will be given to individual projects.
• Grade in letters (e.g., A+, A-, ... ,D+, D, F). A- or above < 30% and B- or above < 90%.