• Email is the preferred method of communication. Class mailing list will be created as [email protected]. But, the announcements will be made in DingTalk group chat.

• 01 (2.18 Tue): Course overview (Syllabus), Python, Github, Etc.
• 02 (2.21 Fri): HSBC Guest Lecture [1/4] Model management cycle in banking industry, Tool setup (GCP/Ali Cloud).
• 03 (2.25 Tue): Brief Python crash course (Basic | Numpy, Notebook Shorcut Keys) | Intro (Slides, Reading: PML Ch. 1) | Notations, Regression (Slides)
• 04 (2.28 Fri): Regression weight update (Slides) PML Ch. 2 (Perceptron, Adaline, Gradient descent, SGD),
• 05 (3.03 Tue): Logistic Regression (Slides, Reading: PML Ch. 3)
• 06 (3.06 Fri):
• 07 (3.10 Tue): SVM/KNN/Decision Tree (Slides, Reading: PML Ch. 3)
• 08 (3.13 Fri):
• 09 (3.17 Tue):
• 10 (3.20 Fri): HSBC Guest Lecture [2/4] Data mining, profiling, visualization, and conclusion.
• 11 (3.24 Tue):
• 12 (3.27 Fri): HSBC Guest Lecture [3/4] Model sharings.
• 13 (3.31 Tue):
• 14 (4.03 Fri): HSBC Guest Lecture [4/4] Practical issues of applying ML to the real world.
• 15 (4.07 Tue): Midterm Exam
• 16 (4.10 Fri):
• 17 (4.14 Tue): Course Project Presentation (Tentative)
• 18 (4.17 Fri): Course Project Presentation

• Course slides: Intro | Regression | SVM/KNN/Tree | SVD/PCA/LDA | Hyperparameter | Neural Network | Graphical Model
• Past Exam: 2017 | 2018
• Exams from Tom Michell's ML course (Carnegie Mellon University)

• Set 0: [Required Software] [Due by 2.22 Sat]

  • Register on Github.com and let TA know your ID (by DingTalk). Make sure to user your full real name in your profile. Accept invitation to the PHBS organization from TA.
    • Create a designated repository GITHUB_ID/PHBS_MLF_2019 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 (available on CMS). Then clone the two repositories 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)
  • Save the screenshot of (1) Github Desktop (showing 2 repositories) (2) Jupyter Notebook (Anaconda) (3) PyCharm (See my example) and make sure to press Push Origin to sync with the online repository in github.com.

• Previous Years: 2018

• Lectures: Tuesday & Friday 1:30 – 3:20 PM
• Venue: Online/DingTalk PHBS Building, Room 229

• Office: PHBS Building, Room 755
• Phone: 86-755-2603-0568
• Email: [email protected]
• Office Hour: Online/DingTalk (TBA)

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

With the advent of computation power and big data, machine learning (ML) recently became one of the most spotlighted research field 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 of 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 will give 4 guest lectures thrroughout the course to demonstrate how ML is developed and shared in banking industry. In the guest lectures, students will also learn how to use cloud computing (Google Cloud Platform/Ali Cloud)

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 undergraduate program.

• 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
• 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

• PML: PHBS/python-machine-learning-book-2nd-edition (forked)
• ISLR-Python: PHBS/ISLR-python (forked) ISRL implemented in Python

• Attendance 20%, Mid-term exam 30%, Assignments 20%, Course Project 30%
• Attendance: TBA Randomly checked. The score is calculated as 20 – 2x(#of absence). Leave request should be made 24 hours before with supporting documents, except for emergency. Job interview/internship cannot be a valid reason for leave
• Mid-term exam: 4.7 Tues. In-class open-book without computer/phone/calculator
• Course project: Data Proposal and Presentation. Group of up to ?? people.
• Attendance: checked randomly. The score is calculated as 20 – 2x(#of absence). Leave request should be made 24 hours before with supporting documents, except for emergency. Job interview/internship cannot be a valid reason for leave
• Grade in letters (e.g., A+, A-, ... ,D+, D, F). A- or above < 30% and B- or below > 10%.