CPSC/AMTH/CBB 745 - Advanced Topics in Machine Learning & Data Mining - Spring 2018 Yale
Yale University CPSC745 - S2018

CPSC/AMTH/CBB 745

Advanced Topics in Machine Learning & Data Mining

Spring 2018

Instructors:Guy Wolf&Smita Krishnaswamy
(guy.wolf@yale.edu)(smita.krishnaswamy@yale.edu)

TA: David van Dijk (david.vandijk@yale.edu)

Modern data analysis methods are expected to handle massive amounts of data that are being collected in a variety of domains, including the biological/biomedical, social, economic, artistic, and cultural domains. This seminar will provide an overview of the advances that have been made in the last decade in machine-learning and automatic data-mining approaches for dealing with the challenges that arise in such settings. This year, the seminar will focus on a broad scope of data analysis tasks, with emphasis on deep learning approaches towards them. Additional approaches, such as kernel methods and dictionary learning, will be included as well to provide a wide perspective on the field.

The seminar will be based on student presentations and discussions of recent prominent publications from leading journals in machine learning (e.g., MLJ, JMLR, TPAMI, and DMKD), computational sciences (e.g., Science, Nature, and PNAS journals), and conferences (e.g., ICML, NIPS, ICLR, and SigKDD/SigMOD). The grades in the seminar will be based on the quality of these presentations (including submitted abstracts and slides), discussions in class, and written summaries.

Class sessions: Wednesdays 2:30-5:15, WTS A60 .


Recommended papers:

A list of recommended papers for student presentations is provided here (link). Notice that this is not meant to be an exhaustive list, but rather as a good starting point for exploring suitable topics. You are more than welcome to use papers that do not appear here, and feel free to consult with the course staff regarding any additional topic/s that you would like to presenting in class.


Presentation signup sheet:

Please sign up for a date to present a topic in class via this Google sheet (link).


Schedule:


Date Subject Presented by Notes
Jan 24
Introduction to the course
 
Guy Wolf & Smita Krishnaswamy Special time: 2:45-5:00
Jan 31 Generative adverserial networks Matt Amodio This session is based on three student talks & discussions
Autoencoders as approximations of markov chains Scott Gigante
Word embeddings Jad Habouch
Feb 07
Scattering Transform
 
Guy Wolf Special time: 2:45-5:00
Feb 14 Spelling Correction and Robust Word Recognition Emily (Xingwen) Guo This session is based on three student talks & discussions
Part of Speech Tagging in NLP Linshan Jiang
Sentiment Analysis Yingfei Zeng
Feb 21 Non-Fixed Time Scale Recurrent Neural Net Paradigms and their Applications Christopher Leet This session is based on two student talks & discussions
Alternative Methods in Longitudinal Data Analysis and Prediction Modeling Jad Habouch
Feb 28 Graph signal processing Jay Stanley This session is based on two guest talks
PHATE: data exploration by visualizing transitions and structure Kevin Moon
Mar 07 TBD Dylan Marshall This session is based on two guest talks
Efficient Methods for Visualization of Large Datasets with t-SNE and Out of Core PCA George Linderman
Mar 14 Spring Break --
Mar 21 Spring Break --
Mar 28 TBD Pei (Mike) Han This session is based on three student talks & discussions
TBD Yiying Zhang
TBD Qi Cao
Apr 04 TBD Stephen Krewson This session is based on three student talks & discussions
TBD Alexander Tong
TBD Daniel Burkhardt
Apr 11 TBD David Chang This session is based on three student talks & discussions
TBD Priyanka Krishnamurthi
TBD Daniel Ehrlich
Apr 18 TBD James Garritano This session is based on two guest talks
TBD TBD
Apr 25 TBD TBD
TBD TBD