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.
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.
- Machine Learning track requires:- Breadth courses
– Required Track courses (6pts)
– Track Electives (6pts)
– General Electives (6pts) - 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.
- 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.
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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 Checklist to keep track of your requirements.
1. BREADTH REQUIREMENT
Visit the breath requirement page for more information.
2. REQUIRED TRACK COURSES
Students must complete two required track courses by either taking two courses from group A, or one course from group A plus one course from group B. (At least one course must be taken from group A). 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.
|
Group |
Course ID |
Title |
| A | COMS W4252 | Introduction to Computational Learning Theory |
| A | COMS W4771 or COMS W4721 or ELEN 4720[1] | Machine Learning OR Machine Learning for Data Science OR Machine Learning for Signals, Information and Data |
| A | COMS W4772 or COMS 6772 | Advanced Machine Learning |
| A | COMS 4995 | Neural Networks Deep Learning |
| A | COMS/STAT G6509/6701 | Foundations of Graphical Models (This course is an advanced course, but MS students may register for it with instructor approval) |
| A | COMS 4732 | Computer Vision II |
| A | COMS 4773 | Machine Learning Theory |
| A | COMS 4774 | Unsupervised Learning |
| A | COMS 4775 | Causal Inference (Previously listed as COMS 4995: Causal Inference) |
| B | COMS W4731 | Computer Vision I |
| B | COMS W4705 | Natural Language Processing |
| B | COMS W4733 | Computational Aspects of Robotics |
| B | COMS W4701 | Artificial Intelligence |
3. ELECTIVE TRACK COURSES
Students are required to take 2 courses from the following list, at least one of which must be a 6000-level course. Student cannot ‘double count’ a course that they took as a required track course as a track elective. 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 |
| CSOR W4246 | Algorithms for Data Science |
| COMS W4705 | Intro to Natural Language Processing |
| COMS W4731 | Computer Vision |
| COMS 4732 | Computer Vision II |
| COMS W4733 | Computational Aspects of Robotics |
| COMS W4737 | Biometrics |
| COMS W4761 | Computational Genomics |
| COMS E4762 | Machine Learning for Functional Genomics |
| COMS W4771 or COMS W4721 or ELEN 4720[1] | Machine Learning OR Machine Learning for Data Science OR Machine Learning for Signals, Information and Data |
| COMS W4772 or COMS 6772 | Advanced Machine Learning (or COMS 6998: Machine Learning Personalization only valid if taken in Spring 2018) |
| COMS W4776 | Machine Learning for Data Science |
| COMS W4995 | Visit the topics courses page to see which COMS 4995 courses apply to this track. |
| COMS E6111 | Advanced Database Systems |
| COMS E6232 | Analysis of Algorithms II |
| 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 (Advisor approval required) |
| COMS E6998 | Visit the topics courses page to see which COMS 6998 courses apply to this track. |
| CSEE E6892 | Bayesian Models in Machine Learning |
| CSEE E6898 | Large-Scale Machine Learning |
| CSEE E6898 | Sparse Signal Modeling |
| APMA E4990 | Modeling Social Data |
| BINF G4006 | Translational Bioinformatics |
| ECBM E4040 | Neural Networks and Deep Learning |
| ECBM E6040 | Neural Networks and Deep Learning Research |
| EECS E6691 | Topics in Data-Driven Analysis & Comp: Advanced Deep Learning |
| EECS E6699 | Topics in Data-Driven Analysis and Computation: Mathematics of Deep Learning |
| EECS E6720 | Bayesian Models of Machine Learning |
| EECS E6870 | Speech Recognition |
| EECS E6893 | Big Data Analytics or Topics-Information Processing (May only count 1 of these) |
| EECS E6895 | Topic Adv Big Data Analytics |
| EECS E6894 | Deep Learning for Computer Vision and Natural Language Processing |
| ELEN 6885 | Reinforcement Learning |
| ELEN E6886 | Sparse Representations and Higher Dimensional Geometry |
| ELEN E6899 | Topics in Information Processing: Autonomous Multi-Agent Systems |
| IEOR E6613 | Optimization I |
| IEOR E8100 | Optimization Methods in Machine Learning |
| IEOR E8100 | Big Data & Machine Learning |
| MECS E6615 | Advanced Robotic Manipulation |
| STAT 4001 (previously known as SIEO 4150) | Introduction to Probability and Statistics |
| STAT W4201/4291/5291 or IEOR 4150 | Probability and Statistics/Advanced Data Analysis |
| STAT W4240* or IEOR 4540 | Data Mining |
| STAT W4282 or STAT GU4205 | Linear Regression/Time Series Analysis/Linear Regression Models |
| STAT W4249/STAT 4243 | Applied Data Science |
| STAT G4400/4241/5241* | Statistical Machine Learning |
| STAT W4640/4224/5224 | Bayesian Statistics |
| STAT 5242 | Advanced Machine Learning |
| STAT W4700 | Probability and Statistics |
| STAT G6101 | Statistical Modeling and Data Analysis I |
| STAT G6104 | Computational Statistics |
| STAT GR8101 | Topics in Applied Statistics: Applied Causality |
*Due to a significant overlap in course material, students in the Machine Learning track can only take 1 of the following courses – ELEN 4903, IEOR 4525, STAT 4240, STAT 4400/4241/5241 – as a track elective or a general elective.
4. GENERAL ELECTIVES
Remaining points toward the MS program must be chosen from either the Track Electives listed above or from the CS department at the 4000-level or higher. Students may also request to use at most 3 points of Non-CS/Non-Track coursework if approved by the process listed below.
- Non-CS/Non-Track: CS MS students may request up to 3 points of Non-CS/Non-Track points to count toward their 30 point MS program. CS Track advisor may review and approve if the course is determined to be relevant to the CS MS track and sufficiently technical in nature. Students should send the Non-CS/Non-Track Form and the course syllabus to your Track Advisor for review.
5. 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.
Please note that some Data Science Institute courses such as COMS W4121 (Computer Systems for Data Science) do not count towards the CS MS degree. If you have any questions, please contact your advisor or the CS Student Services.
As of Spring 15, STAT W4252 Introduction to Data Science is no longer an approved track elective course.
** Known Non-Track Course**
CSOR E4995 Financial Software Systems
6. TRACK ADVISORS
Please check your MICE profile for your track advisor assignment. All questions regarding your track can be sent directly to your assigned faculty advisor. ML track has several Track Advisors available however your advisor name can be found on your MICE account.
7. GRADUATION
Candidates preparing for graduation should submit a completed application for degree to the Registrar’s Office and submit a track graduation form/checklist to CS Student Services.
Last updated Jan 26th 2023
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