• Midterm exam solution is uploaded
• Midterm exam on Friday will be in Rm 321
• Python Crash Course will be on 9.12 (Wed) 1:30 PM. Class mailing list created.
• Email is the preferred method of communication. Class mailing list will be created as [email protected].

• OscarHemmingsen:
• joelgysel:
• Lillahbenard:
• Davethedata: Company default
• XiaoqianZhu1997
• MatteoRossiTantini
• TianXiangxun
• qwerjeff: Musical instrument classification from Midi file
• raphalecui1990: Stock price prediction from demand elasticity
• nico-busch:
• sergeyivanov01: Oil price prediction
• HuangJing1801: Loan credit evaluation from Lending Club data
• devon-ge: Colorizing grayscale image (CNN)
• RasmusAU: Predicting movie success

• 18 (11.09 Fri) Course Project Presentation
• 17 (11.06 Tues): Tensorflow / Keras (XOR, Iris based on here)
• 16 (11.02 Fri): Neural Network (Slides), ML-related research presentation (LOOLSM)
• 15 (10.30 Tues): Use of sklearn in PML Ch. 10, Neural Network (Slides)
• 14 (10.26 Fri): Use of sklearn in PML Ch. 5/6/7
• 13 (10.23 Tues): Course Project Proposal
• 12 (10.19 Fri): Midterm exam (Rm 321) (Solution)
• NO CLASS on 10.16 Tues
• 11 (10.12 Fri): Confusion matrix, ROC curve, LOOCV (Slides)
• 10 (10.09 Tues): SVD/PCA/LDA (Slides) PML Ch. 5, Hyperparameters (Slides) PML Ch. 6
• 09 (09.28 Fri): Data Preprocessing PML Ch. 4, SVD/PCA/LDA (Slides) PML Ch. 5
• 08 (09.25 Tues): Kernel SVM/Bagging/RF (Slides) PML Ch. 3
• 07 (09.21 Fri): SVM/KNN/Decision Tree (Slides) PML Ch. 3
• 06 (09.18 Tues): Logistic Regression (Slides) PML Ch. 3
• 05 (09.14 Fri): PML Ch. 2 (Perceptron, Adaline, Gradient descent, SGD), Regression weight update (Slides)
• 04 (09.12 Wed instead of 10.16 Tues): Python crash course (Basic | Numpy). More cheatsheets also available in MLF CMS.
• 03 (09.11 Tues): ISLR Ch. 3, PML Ch. 1
• 02 (09.07 Fri): Intro (Slides), Regression (Slides)
• 01 (09.04 Tues): Course overview (Syllabus), Python, Github, Etc.

• Course slides: Intro | Regression | SVM/KNN/Tree | SVD/PCA/LDA | Hyperparameter | Neural Network | Graphical Model
• Past Slides (All-in-one): 2017-18
• Past Exam: 2017-18
• Exams from Tom Michell's ML course (Carnegie Mellon University)

• Set 1 [Due by 09.11 Tues]

  • Register on Github.com and let TA and me know your ID. Give your full name in your profile. Accept invitation to the PHBS organization from TA. Install Github Desktop (available on CMS).
  • Install Anaconda Python distribution (3.X version, 2.X version). Anaconda distribution is core Python + useful scientific computation libraries (e.g., numpy, scipy, pandas) + package management system (pip or conda)
  • Send the screenshot of both softwares installed to TA. (Example: Github Desktop, Anaconda Spyder)

• Data Proposal [10.23 Tues class]

• Presentation [11.09 Fri class]

• Lectures: Tuesday & Friday 8:30 AM – 10:20 AM
• Venue: PHBS Building, Room 211

• Office: PHBS Building, Room 755
• Phone: 86-755-2603-0568
• Email: [email protected]
• Office Hour: Tues & Fri 10:30 – 11:30 AM or by appointment

• Email: [email protected]
• TA Office Hour: Tues & Fri 3:30 - 5:30 PM (Room 213/214)

With the advent of computation power and big data, machine learning recently became one of the most spotlighted research field in industry and academia. This course provides a broad introduction to machine learning in theoretical and practical perspectives. Through this course, students will learn the intuition and implementation behind the popular machine learning 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.

Undergraduate-level knowledge in probability/statistics and previous experience in programming language (python) is highly recommended.

• 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
• JPM-AI: Big Data and AI Strategies by J.P. Morgan

• 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%
• Mid-term exam: 10.19 Fri. Open-book exam without computer/phone/calculator use
• Course project: Data Proposal (10.23 Tues) and Presentation (11.09 Fri). Group of up to 4 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 C+ or below > 10%.