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The official description sets scope
The Rutgers source establishes the stable academic frame for Introduction To Deep Learning; the section syllabus controls current details.
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01:198:462 · Computer Science · School 01 — New Brunswick School of Arts and Sciences
Instructor Profile: Ahn, Sungjin Prerequisite Information: Prerequisites: M 250 (Introductory Linear Algebra), CS 112 (Data Structures), CS 206 (Introduction to Discrete Structures II) OR M 477 (Mathematical Theory of Probability) OR S 379 (Basic Probability Theory) Learning Goals: The course objectives are (1) to understand the principles of deep learning and its capabilities and (2) to acquire practical skills to design, implement, and train practical deep learning systems.
Can I take it?
Intro Linear Algebra · Data Structures · Introduction To Discrete Structures II
Students rate teaching
SIRS · 178 responses · 2022–2024
Spring 2027
2 open in snapshot · 2 instructors
When it meets
Busch
Checked-in sections · current seat counts are unavailable.
AI-generated overview
3 units · topic sequence may vary by instructor
Establish the official scope of Introduction To Deep Learning and the vocabulary needed to read the current section syllabus accurately.
Work through the central evidence, methods, or practices named by the official description without assuming a section-specific sequence.
Connect course concepts, test their limits, and use the current instructor’s guidance to determine the required form of analysis or practice.
The Rutgers source establishes the stable academic frame for Introduction To Deep Learning; the section syllabus controls current details.
Use the forms of evidence and reasoning appropriate to the discipline rather than relying on title-level assumptions.
Verify prerequisites, materials, assessment rules, and deadlines against the active term and instructor.
Common pitfall
Instead
Use the current section syllabus for readings, assignments, grading, and schedule.
Common pitfall
Instead
Define unfamiliar terms early and connect each term to a concrete course example.
Common pitfall
Instead
State what the course evidence supports and where the conclusion remains limited.
2 open · Busch
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0114539 | Open | Scarvelis | Tuesday 2:00 PM-3:20 PM at PH 115; Thursday 2:00 PM-3:20 PM at PH 115; Tuesday 5:55 PM-6:50 PM at SEC 205PH 115SEC 205 | Busch |
| 0214540 | Open | Hao Wang | Tuesday 2:00 PM-3:20 PM at PH 115; Thursday 2:00 PM-3:20 PM at PH 115; Tuesday 5:55 PM-6:50 PM at SEC 203PH 115SEC 203 | Busch |
Historical student surveys
Teaching
4.30
Course quality
4.22
Response rate
48.4%
Coverage
6 offerings · 2022–2024
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Hao Wang | 3 | 4.13 | 4.20 |
| Charles Cowan | 2 | 4.75 | 4.40 |
| Ahmed Elgammal | 1 | 3.90 | 3.90 |
Catalog and planning context
Credits
4
Current campuses
Busch
Current availability
2 open of 2
Catalog terms
Fall, Spring
Core codes
None listed
Loaded terms
4
(01:640:250 and 01:198:112 and 01:198:206 and 01:198:206) <em> OR </em> (01:640:250 and 01:198:112 and 01:640:477 and 01:640:477) <em> OR </em> (01:640:250 and 01:198:112 and 01:960:379 and 01:960:379)
No direct degree-list membership appears in the checked-in requirement index.