Machine Learning and AI

Course Directors: David Fenyo & Kasthuri Kannan, New York University (NYU)
Kasthuri's Coordinates: TRB 737, 227, E.30th St. New York
E-mail at kasthuri.kannan [at] nyumc.org (no personal emails please!)
Information Lectures

Lecture Schedule

Sept. 24, Oct. 01, Oct. 04, Oct. 08, Oct. 25 and Oct. 29 at 5.30 pm (Mondays)/5.30 pm (Thursdays)
Location: Science Building, 345 East 30th St, Ground Floor, Room G06

Course Information

Official Course Name: Machine Learning (BMSC-GA 4439 and BMIN-GA 1004)

Official Course Page: Click here

Learning Objectives

The student will learn, understand and work on the most commonly used machine learning methods and will be introduced to Artificial Intelligence (AI) through an hands-on tutorial.

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 Material

Required Reading:

Introduction to Statistical Learning: with Applications in R. James G, Witten D, Hastie T, Tibshirani R. 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

Reading

The following course material are recommended reading for the four lectures below:

Performance Estimation & Regularization
An Introduction to Statistical Learning by Gareth James et al. Chapters 5 & 6
Additonal Reading
The Elements of Statistical Learning by Hastie et al. Chapter 7
Tree-Based Methods/Ensemble Schemes
An Introduction to Statistical Learning by Gareth James et al. Chapter 8
Additonal Reading
1. Carter H, Chen S, Isik L, et al. Cancer-specific High-throughput Annotation of Somatic Mutations: computational prediction of driver missense mutations. Cancer research. 2009
2. Waks Z, Weissbrod O, Carmeli B, Norel R, Utro F, Goldschmidt Y. Driver gene classification reveals a substantial overrepresentation of tumor suppressors among very large chromatin-regulating proteins. Scientific Reports. 2016
Support Vector Machines
An Introduction to Statistical Learning by Gareth James et al. Chapter 9
Additonal Reading
1. Hyeran Byun and Seong-Whan Lee, Applications of Support Vector Machines for Pattern Recognition: A Survey, SVM 2002, LNCS 2388, 2002
2. Mao Y, Chen H, Liang H, Meric-Bernstam F, Mills GB, Chen K. CanDrA: Cancer-Specific Driver Missense Mutation Annotation with Optimized Features. PLoS ONE. 2013
Markov Models
Dugad and Desai, A tutorial on hidden markov models
Link: https://pdfs.semanticscholar.org/9777/7097ab553b0b9e2d9461f956affba3cb5e30.pdf
Additonal Reading
Olivier Cappé, Eric Moulines, and Tobias Ryden. Inference in Hidden Markov Models. Springer Series in Statistics.

Homeworks and Tutorials

Contact: Anna Yeaton (Anna.Yeaton@nyulangone.org)

Lectures

Supervised Learning: Classification NA Slides NA
Performance Estimation & Regularization YouTube Slides Tutorial/Lab
Tree-Based Methods/Ensemble Schemes YouTube Slides Tutorial/Lab
Support Vector Machines YouTube Slides Tutorial/Lab
Markov Models YouTube Slides Tutorial/Lab
Feature selection NA Slides NA
Introduction to AI NA NA Tutorial/Lab