Machine Learning Fall 2020
 
 
The Sackler Institute of Graduate Biomedical Sciences at NYU School of Medicine
 
Machine Learning Fall 2020 (BMSC-GA 4439 and BMIN-GA 1004)

Course Directors:
David Fenyö (David@FenyoLab.org)
Wenke Liu (Wenke.Liu@nyulangone.org)

Teaching Assistants:
Anna Yeaton (Anna.Yeaton@nyulangone.org)
Runyu Hong (Runyu.Hong@nyulangone.org)

Learning objectives

The student will learn and understand the most commonly used machine learning methods.

Course Material

Required Reading:

  • Introduction to Statistical Learning: with Applications in R. James G, Witten D, Hastie T, Tibshirani R. Springer 2013.
  • Applied Predictive Modeling by Max Kuhn & Kjell Johnson, Springer 2013.
Recommended Reading:
  • Pattern Classification, 2nd Edition,Richard O. Duda, Peter E. Hart, David G. Stork, ISBN: 978-0-471-05669-0
  • The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Hastie T, Tibshirani R, Friedman J. Springer: 2011.
  • Pattern Recognition and Machine Learning (Information Science and Statistics) by Christopher Bishop (Author) ISBN-10: 0387310738

General Policies

Late/missed work: You must adhere to the due dates for all required submissions. If you miss a deadline, then you will not get credit for that assignment/post.

Incompletes: No "Incompletes" will be assigned for this course unless we are at the very end of the course and you have an emergency.

Responding to Messages: I will check e-mails daily during the week, and I will respond to course related questions within 48 hours.

Announcements: I will make announcements throughout the semester by e-mail.

Make sure that your email address is updated; otherwise you may miss important emails from me.

Safeguards: Always back up your work on a safe place (electronic file with a backup is recommended) and make a hard copy. Do not wait for the last minute to do your work. Allow time for deadlines.

Plagiarism: Plagiarism, the presentation of someone else's words or ideas as your own, is a serious offense and will not be tolerated in this class. The first time you plagiarize someone else's work, you will receive a zero for that assignment. The second time you plagiarize, you will fail the course with a notation of academic dishonesty on your official record.

Course Assessment

  • Attendance/Participation (40%)
  • Homework (20%)
  • Final Project (40%)
Lectures

Lecture 1 Course Overview (September 10, 2020 3-4:30pm)
Lecturer: David Fenyo

Lecture 2 Technical Introduction (Rstudio, Pycharm, VScode, Linux, HPC, SLURM, Google Colab, Jupyter notebook) (September 15, 2020 3-4:30pm)
Lecturer: Runyu Hong

Lecture 3 Exploring Data: CPTAC endometrial clinical data (September 17, 2020 3-4:30pm)
Lecturer: Wenke Liu

Lecture 4 Exploring Data: Clustering (September 24, 2020 3-4:30pm)
Lecturer: Wenke Liu

Lecture 5 Exploring Data: Dimension Reduction I (September 29, 2020 3-4:30pm)
Lecturer: Wenke Liu

Lecture 6 Exploring Data: Dimension Reduction II (October 1, 2020 3-4:30pm)
Lecturer: Wenke Liu

Lecture 7 Student Project Plan Presentation (October 6, 2020 3-4:30pm)


Lecture 8 Supervised Learning: Regression (October 8, 2020 3-4:30pm)
Lecturer: Wilson McKerrow

Lecture 9 Supervised Learning: Classification (October 13, 2020 3-4:30pm)
Lecturer: Anna Yeaton

Lecture 10 Supervised Learning: Regularization (October 15, 2020 3-4:30pm)
Lecturer: Anna Yeaton

Lecture 11 Markov Models (October 20, 2020 3-4:30pm)
Lecturer: Wilson McKerrow

Lecture 12 Feature selection (October 22, 2020 3-4:30pm)
Lecturer: Zhi Li

Lecture 13 Student Project Exploratory Data Analysis Presentation (October 27, 2020 3-4:30pm)


Lecture 14 Student Project Exploratory Data Analysis Presentation (October 29, 2020 3-4:30pm)


Lecture 15 Expectation Maximization (November 3, 2020 3-4:30pm)
Lecturer: Wilson McKerrow

Lecture 16 Tree-Based Methods (November 5, 2020 3-4:30pm)
Lecturer: Wenke Liu

Lecture 17 Support Vector Machines (November 10, 2020 3-4:30pm)
Lecturer: Wenke Liu

Lecture 18 Neural Networks (November 12, 2020 3-4:30pm)
Lecturer: Runyu Hong

Lecture 19 Machine Learning Applied to Healthcare (November 17, 2020 3-4:30pm)
Lecturer: Narges Razavian

Lecture 20 Machine Learning Applied to Text Data (November 19, 2020 3-4:30pm)
Lecturer: Stephen Johnson

Lecture 21 Machine Learning Applied to Omics Data (November 24, 2020 3-4:30pm)
Lecturer: Kelly Ruggles

Lecture 22 Student Project Presentation (December 8, 2020 3-4:30pm)


Lecture 23 Student Project Presentation (December 10, 2020 3-4:30pm)


Lecture 24 Student Project Presentation (December 15, 2020 3-4:30pm)