PSAM

5020

Machine Learning

Parsons School of Design: Art, Media & Technology

Non-Liberal Arts

Undergraduate Course

Graduate Course

Degree Students

Machine Learning

Spring 2021

Taught By: Seth Kranzler

Section: A

CRN: 3871

Credits: 3

Machine learning is the systematic study of algorithms and systems that improve their knowledge and performance with experience. Collecting and analyzing data through machine learning algorithms and models can uncover complex patterns in massive amounts to data to make more accurate predictions and to reveal coherent dimensions. In this course students will consider classification, regression, clustering, subgroup, and association models to predict and describe, using supervised and unsupervised learning methods. Special attention will be given to models and techniques that aid and support the visualization of complex data.

Open to: All University Graduate students.

College: Parsons School of Design (PS)

Department: Art, Media & Technology (PSAM)

Campus: New York City (GV)

Course Format: Studio (S)

Max Enrollment: 15

Add/Drop Deadline: N/A
Online Withdrawal Deadline: N/A

Seats Available: Yes

Status: Open*

*Status information is updated every five minutes. The status of this course may have changed since the last update. Open seats may have restrictions that will prevent some students from registering. Updated: 3:06pm 7/4/2020 EDT

Meeting Info:

Days: Wednesday

Times: 12:10pm - 2:50pm

Building: Academic Entrance 63 Fifth Ave

Room: 300

Date Range: 1/20/2021 - 5/5/2021

Machine Learning

Spring 2021

Taught By: Seth Kranzler

Section: B

CRN: 8464

Credits: 3

Machine learning is the systematic study of algorithms and systems that improve their knowledge and performance with experience. Collecting and analyzing data through machine learning algorithms and models can uncover complex patterns in massive amounts to data to make more accurate predictions and to reveal coherent dimensions. In this course students will consider classification, regression, clustering, subgroup, and association models to predict and describe, using supervised and unsupervised learning methods. Special attention will be given to models and techniques that aid and support the visualization of complex data.

Open to: All University Graduate students.

College: Parsons School of Design (PS)

Department: Art, Media & Technology (PSAM)

Campus: New York City (GV)

Course Format: Studio (S)

Max Enrollment: 15

Add/Drop Deadline: N/A
Online Withdrawal Deadline: N/A

Seats Available: Yes

Status: Open*

*Status information is updated every five minutes. The status of this course may have changed since the last update. Open seats may have restrictions that will prevent some students from registering. Updated: 3:06pm 7/4/2020 EDT

Meeting Info:

Days: Friday

Times: 4:00pm - 9:40pm

Building: TBD

Room: TBD

Date Range: 1/22/2021 - 5/7/2021

Machine Learning

Fall 2020

Taught By: Aaron Hill

Section: A

CRN: 9013

Credits: 3

Machine learning is the systematic study of algorithms and systems that improve their knowledge and performance with experience. Collecting and analyzing data through machine learning algorithms and models can uncover complex patterns in massive amounts to data to make more accurate predictions and to reveal coherent dimensions. In this course students will consider classification, regression, clustering, subgroup, and association models to predict and describe, using supervised and unsupervised learning methods. Special attention will be given to models and techniques that aid and support the visualization of complex data.

Open to: All University Graduate students.

College: Parsons School of Design (PS)

Department: Art, Media & Technology (PSAM)

Campus: New York City (GV)

Course Format: Studio (S)

Max Enrollment: 18

Add/Drop Deadline: September 14, 2020 (Monday)

Online Withdrawal Deadline: November 22, 2020 (Sunday)

Seats Available: Yes

Status: Waitlist*

*Status information is updated every five minutes. The status of this course may have changed since the last update. Open seats may have restrictions that will prevent some students from registering. Updated: 3:06pm 7/4/2020 EDT

Meeting Info:

Days: Monday

Times: 12:10pm - 2:50pm

Building: TBD

Room: TBD

Date Range: 8/31/2020 - 12/14/2020