Machine Learning for Finance (FN 570) 2023-24 Module 3 (Spring 2024)

Announcements

• The WeChat group will be created by TA. (No 1-to-1 chat please.)
• Email is the preferred method of communication. The class mailing list will be created as [email protected].

Course Resources

• Course slides: Intro | Regression | SVM/KNN/Tree | SVD/PCA/LDA | Hyperparameter | Sentiment / Word Embedding | Neural Network | Graphical Model | Finance Research
• Project: Current | 2022 | 2021 | 2019 | 2018 | 2017 | 2016
• Past years' exam: 2023 | 2022 | 2021 | 2019 (online take-home) | 2018 | 2017 | Exams from Tom Michell's ML course (Carnegie Mellon University)

Lectures:

No	Date	Contents
01	2.20 Tue	Course overview (Syllabus) | Required software (Python, Github, PyCharm) | Python crash course (Basic, Numpy (Notebook Shorcut Keys), Pandas. Also see Datacamp, CheatSheet)
02	2.23 Fri	PML Ch. 1: Intro (Slides) | Notations, Regression, Weight update (Slides)
03	2.27 Tue	PML Ch. 2: Perceptron, Adaline, Gradient descent, Stochastic Gradient Descent
04	3.01 Fri	PML Ch. 3: Logistic Regression (LR) (Slides) and Support Vector Machine (SVM) (Slides)
05	3.05 Tue	PML Ch. 3: KNN (Slides, Code), Decision Tree (Slides).
06	3.08 Fri	PML Ch. 4: Data Preprocessing, PML Ch. 5: SVD/PCA (Slides)
07	3.12 Tue	PML Ch. 5: LDA (Slides), PML Ch. 6: Bias-Variance, Cross-validation (Slides)
08	3.15 Fri	PML Ch. 6: Hyperparameter tuning, Evaluation Metric, Class imbalance (Slides)
09	3.19 Tue	PML Ch. 7: Ensenble Learning (Slides), Kernel Method (Slides, PML Ch 3, 5)
14	3.22 Fri	HSBC Guest Lecture [1/4]: Overview and data introduction.
10	3.26 Tue	PML Ch. 8: Sentiment Analysis (Slides)
11	3.29 Fri	Topics in Finance ML: Recession prediction (Slides), ML in Finance Research (Slides), Collaborative Filtering (Slides)
12	4.02 Tue	Neural Network, Deep Learning, CNN (Slides, PML Ch. 12-15)
13	4.07 Sun	Midterm Exam (In Class)
15	4.09 Tue	HSBC Guest Lecture [2/4]
16	4.12 Fri	HSBC Guest Lecture [3/4]
17	4.16 Tue	HSBC Guest Lecture [4/4]
18	4.19 Fri	Course Project Presentation (may be scheduled later)

Homeworks:

• Set 0: [Required Software] [Due by Friday]

  • Register on Github.com and let TA know your ID. Make sure to use your full real name in your profile. Accept TA's invitation to the PHBS organization.
    • Create a designated repository GITHUB_ID/PHBS_MLF_2023 for your HW and project. Tick Initialize this repository with a README and select python under .gitignore
    • Fork PML repository to your repository.
  • Install Github Desktop. Then clone the PML repository to your local storage.
  • Install Anaconda Python distribution (3.X version, not 2.X version). Anaconda distribution is core Python + useful scientific computation libraries (e.g., numpy, scipy, pandas) + package management system (pip or conda)
  • Install PyCharm Community version. (Or Professional version after applying for free student license)
  • Send to TA the screenshots of (1) Github Desktop (showing the PML repository) (2) Jupyter Notebook (Anaconda) (3) PyCharm (See my examples: Github Desktop, Anaconda Spyder).

• Set 1: [Classifiers] [Due by 3.19 Tues]

  • The goal of this HW is to be familiar with the basic classifiers PML Ch 3.
  • For this HW, we will use Give Me Some Credit on Kaggle. You may download it from the Kaggle link or CMS.
  • Load cs-training.csv into a Pandas dataframe.
  • Fill-in the missing values (nan) with the column means. (Use pd.fillna() or See Ch 4 of PML)
  • Select the 2 most important features using LogisticRegression with L1 penalty. (Adjust C until you see 2 features)
  • Using the 2 selected features, apply LR / SVM / decision tree. Try your own hyperparameters (C, gamma, tree depth, etc) to maximize the prediction accuracy. (Just try several values. You don't need to show your answer is the maximum.)
  • Visualize your classifiers using the plot_decision_regions function from PML Ch. 3
  • Put your result in YOUR_GITHUB_ID/Give-Me-Some-Credit/code/Classifiers.ipynb

• Set 2: [PCA/Hyperparameter/CV] [Due by 3.29 Fri]

  • The goal of this HW is to be familiar with PCA (feature extraction), grid search, pipeline, k-fold CV.
  • For this HW, we continue to use Give Me Some Credit on Kaggle.
  • Extract a few (>2) features using PCA method.
  • Using the selected features from above, we are going to apply LR / SVM / decision tree (or any other algorithm).
  • Implement the methods using pipeline. (PML p185)
  • Use grid search for finding optimal hyperparameters. (PML p199). In the search, apply 5-fold cross-validation.

---

Syllabus

Classes:

• Lectures: Tuesday & Friday 1:30 – 3:20 PM
• Venue: PHBS Building, Room 313

Instructor: Jaehyuk Choi

• Office: PHBS Building, Room 755
• Phone: 86-755-2603-0568
• Email: [email protected]
• Office Hour: Monday 7-9 PM

Teaching Assistance: 苏南 (Nan SU)

• Email: [email protected]
• TA Office Hour (Room 213/214): TBA

Course overview

With the advent of computation power and big data, machine learning (ML) recently became one of the most spotlighted research fields in industry and academia. This course provides a broad introduction to ML in theoretical and practical perspectives. Through this course, students will learn the intuition and implementation behind the popular ML methods and gain hands-on experience in using ML software packages such as SK-learn and Tensorflow. This course will also explore the possibility of applying ML to finance and business. Each student is required to complete a final course project. This year, the compliance analytics team in HSBC bank (Gunagzhou) will give 4 guest lectures to demonstrate how ML is developed and shared in banking industry.

Prerequisites

This course assumes prior knowkedge in probability/statistics and experience in Python. This course is ideally recommended for those who have taken introductory ML/AI courses from an undergraduate program.

Textbooks and Reading Materials

Primary textbook

• PML (primary textbook): Python Machine Learning 3rd Ed. by Sebastian Raschka.
  • Github (PHBS fork)

Other books and online courses

• ISLR: An Introduction to Statistical Learning (with Applications in R) by James, Witten, Hastie, and Tibshirani
  • Python Implementation: PHBS/ISLR-python (PHBS fork)
• Bishop: Pattern Recognition and Machine Learning by Bishop (Microsoft)
• ESL: The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman
• CML: Coursera Machine Learning by Andrew Ng
• DL: Deep Learning by Goodfellow, Bengio, and Courville
• AFML: Advances in financial machine learning by López de Prado

Assessment / Grading Details

• Attendance 20%, Mid-term exam 30%, Assignments 20%, Course Project 30%
• Attendance: Randomly checked. The score is calculated as 20 – 2x(#of absence). Leave requests should be made 24 hours before with supporting documents, except for emergencies. Job interview/internship cannot be a valid reason for leave.
• Mid-term exam: 11.1 Mon. In-class open-book without computer/phone/calculator
• Course project: Data Proposal and Presentation. Group of up to ?? people.
• Grade in letters (e.g., A+, A-, ... ,D+, D, F). A- or above < 30% and B- or below > 10%.