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: Online (DL)

Sync Type: N/A

Course Format: Studio (S)

Max Enrollment: 18

Add/Drop Deadline: February 1, 2021 (Monday)

Online Withdrawal Deadline: April 13, 2021 (Tuesday)

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: 2:37am 11/26/2020 EST

Meeting Info:

Days: Wednesday

Times: 7:00pm - 9:40pm

Building: Online Course

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

Machine Learning

Spring 2021

Taught By: Daniel Sauter

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: Online (DL)

Sync Type: N/A

Course Format: Studio (S)

Max Enrollment: 30

Add/Drop Deadline: January 24, 2021 (Sunday)

Online Withdrawal Deadline: April 27, 2021 (Tuesday)

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: 2:37am 11/26/2020 EST

Meeting Info:

Days: Wednesday

Times: 7:00pm - 9:40pm

Building: Online Course

Date Range: 1/20/2021 - 3/10/2021

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: Online (DL)

Sync Type: N/A

Course Format: Studio (S)

Max Enrollment: 19

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

Online Withdrawal Deadline: November 22, 2020 (Sunday)

Seats Available: Yes

Status: Closed*

* 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: 2:36am 11/26/2020 EST

Meeting Info:

Days: Monday

Times: 12:10pm - 2:50pm

Building: Online Course

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