The Machine Learning Track

The Machine Learning Track is intended for students who wish to develop their knowledge of machine learning techniques and applications. Machine learning is a rapidly expanding field with many applications in diverse areas such as bioinformatics, fraud detection, intelligent systems, perception, finance, information retrieval, and other areas.

1. Summary of Requirements

Machine Learning track students must complete a total of 30 points and must maintain at least 2.7 overall GPA in order to be eligible for the MS degree in Computer Science.

  1. Machine Learning track requires:

    - Breadth courses
    - Required Track courses (6pts)
    - Track Electives (6pts)
    - General Electives (6pts)

  2. Students must take at least 6 points of technical courses at the 6000-level overall. One of the Track Electives courses has to be a 3pt 6000-level course from the Track Electives list (Section 4).

  3. If the number of points used to fulfill the above requirements is less than 30, then General Elective graduate courses at 4000 level or above must be taken so that the total number of credits taken is 30.

  4. Students using previous courses to fulfill track requirements may complete the 30 graduate points by expanding their electives selected from (a) the list of required track courses; (b) the list of Track Elective courses; or (c) other graduate courses.

Please use the Degree Progress Check to keep track of your requirements.

2. Breadth Requirement

Students are required to satisfy the Breadth Requirement by taking 1 course from Group 1, 1 course from Group 2, 1 course from Group 3, and 1 more course from any of the three groups. Track courses taken at Columbia can also satisfy the breadth requirement.

Group  Courses
Group 1 (Systems)

All CS 41xx courses except CS 416x and CS 417x
All CS 48xx courses, and CS 4340, 4444, and 4460

Group 2 (Theory)

All CS 42xx courses including CSOR W4231

Group 3 (AI and Apps)

All CS 47xx courses, and CS 416x and CS 417x


3. Required Track Courses

Students are required to complete two (2) of the following courses. Students who have taken equivalent courses in the past and received grades of at least a B may apply for waivers and take other CS courses instead.

Course ID

Title

COMS W4252

Introduction to Computational Learning Theory

COMS W4771

Machine Learning

COMS W4772

Advanced Machine Learning


4. Elective Track Courses

Students are required to take two courses from the following list, at least one of which must be a 6000-level course. Other courses on this list may be used as General Electives or to replace required track courses when the student has received a waiver.

Course ID

Title

COMS W4111

Introduction to Databases

COMS W4252

Introduction to Computational Learning Theory 

COMS W4705

Intro to Natural Language Processing

COMS W4731

Computer Vision

COMS W4737

Biometrics

COMS W4761

Computational Genomics

COMS W4771

Machine Learning 

COMS W4772

Advanced Machine Learning 

COMS W4995

Intro Social Networks

COMS E6111

Advanced Database Systems

COMS E6253

Advanced Topics in Computational Learning Theory

COMS E6717 (ELEN E6717)

Information Theory

COMS E6735

Visual Databases

COMS E6737

Biometrics

COMS E6901

Projects in Computer Science

COMS E6998

Search Engine Technology

COMS E6998

Network Theory

COMS E6998

Algorithmic Game Theory

COMS E6998

Statistical Methods for NLP

COMS E6998

NLP for the Web

COMS E6998

Advanced Topic in Machine Learning

COMS E6998

Machine Translation

COMS E6998

Machine Learning for NLP

COMS E6998

Intro/Distributed Data Mining

COMS E6998

Analysis of social Info. Nets

COMS E6998

Algorithms/Deal/Massive Data

COMS E6998

Econ of Social Networks

COMS E6998

CV and ML an Mobile Platforms

COMS E6998

Data Science & Entrepreneurship

COMS E6998

Fund of Speaker Recognition

COMS E6998

Bayesian Analysis for NLP

COMS E6998

Sublinear Time Algos Learning

COMS E6998

Semantic Tech in IBM Watson

COMS E6998

Cloud and Big Data

COMS E6998

Digitally Mediated Storytelling

CSEE E6892

Bayesian Models in Machine Learning

CSEE E6898

Large-Scale Machine Learning

CSEE E6898

Sparse Signal Modeling

IEOR E6613

Optimization I

IEOR E8100

Optimization Methods in Machine Learning

SIEO 4150 or STAT W4201

Probability and Statistics/Advanced Data Analysis

STAT W4240

Data Mining

STAT W4242

Introduction to Data Science

STAT W4249

Applied Data Science

STAT G4400

Statistical Machine Learning

STAT G6101

Statistical Modeling and Data Analysis I

STAT G6104

Computational Statistics


5. General Electives

Students are required to complete at least 6 additional graduate points at, or above, the 4000 level; at least 3 of these points must be CS, the other 3 points may be non-CS/non-technical course approved by the track advisor. Candidates who wish to take a non-CS/non-Technical course should complete a non-tech approval form, get the advisor's approval, and submit it to Janine Maslov or Remi Moss. At most 3 points overall of the 30 graduate points required for the MS degree may be non-CS/non-technical. 

6. Track Planning


Please visit the Directory of Classes to get the updated course listings. Please also note that not all courses are offered every semester, or even every year. A few courses are offered only once every two or three years or even less frequently. For more information, please see the SEAS Bulletin CS course-offering schedule (This schedule can change due to unforeseeable circumstances; thus, it should only be used as a reference).

Please note that the Institute of Data Science and Engineering (IDSE) courses such as COMS W4721 Machine Learning for Data Science do not count towards the MS degree in Computer Science. If you have any questions about this, please contact the Computer Science Student Services.

7. Track Advisor

Please direct all questions concerning the Machine Learning track Prof. and Prof. .

8. Graduation

Candidates preparing for graduation should submit a completed application for degree to the Registrar's Office and submit a track graduation form to CS Student Services.


Last updated 1/28/2014.