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01:198:445 · Computer Science · School 01 — New Brunswick School of Arts and Sciences
CS445 is Rutgers Computer Science's upper-level variable-topics course. The department describes it as advanced topics in computer science that change with faculty and student interests, and the public syllabus currently linked from the official course page is a machine learning offering.
Can I take it?
How hard?
Students rate teaching
SIRS · 33 responses · 2020–2025
Spring 2025
0 open in snapshot · 1 instructor
When it meets
Busch
Checked-in sections · current seat counts are unavailable.
TTh 5:40 PM · Busch · 1 section · 0 open
AI-generated overview
Higher means more demanding · AI summary
5 units · topic sequence may vary by instructor
The first thing to understand about CS445 is that the number itself is generic. The official synopsis says the topic can change, so students should always read the current section subtitle and syllabus before assuming the class resembles an older 445. In the official public syllabus linked from the department page, the semester begins with machine-learning goals and a readiness check around programming, probability, calculus, and linear algebra.
The machine-learning version of 445 starts with the core supervised-learning setup: data, targets, loss, risk, and empirical risk minimization. This is the conceptual frame that lets later algorithms feel like variations on a small number of recurring ideas rather than a bag of unrelated models.
A large middle section is about how practical models are fit. The linked syllabus moves through linear regression, maximum likelihood, gradient descent, generalized linear models, logistic regression, overfitting, and regularization. The recurring question is not just which formula to use, but how optimization and generalization interact.
Later units broaden the model family. The official plan includes large-margin methods, support vector machines, decision trees, ensemble methods, generative classifiers, mixture models, and expectation maximization. By this point students are usually comparing assumptions and tradeoffs rather than learning one single 'best' model.
The end of the linked syllabus introduces deep learning, backpropagation, domain applications, dimensionality reduction, clustering, and online learning. That makes the course feel like a technical bridge: students leave with both classical machine-learning vocabulary and a map of where more specialized AI or data courses go next.
Public course-specific student discussion is limited for CS445, and the course number is explicitly variable-topics. Students should not generalize a machine-learning offering's workload or difficulty to every future 445 section; the current Rutgers-linked syllabus and instructor expectations are the reliable guide.
Unlike a fixed-core class such as Data Structures or Formal Languages, CS445 is intentionally a topics slot. Students should treat the current official syllabus as the real contract.
Many different algorithms can be understood as choosing a representation, a loss, and a procedure for minimizing that loss.
A model can fit the training set extremely well and still generalize poorly, which is why regularization and evaluation matter so much.
When the topic is machine learning, comfort with vectors, derivatives, probability, and implementation work usually matters more than memorizing buzzwords.
Common pitfall
Instead
Check the current Rutgers section subtitle and syllabus first; 445 is a reusable advanced-topics slot.
Common pitfall
Instead
Keep asking what loss is being optimized, what assumptions the model makes, and how overfitting is controlled.
Common pitfall
Instead
Brush up on linear algebra, probability, and derivatives before the semester if the current topic is machine learning or another mathematically technical area.
0 open · Busch
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0123533 | Closed | Matthew Stone | Tuesday 5:40 PM-7:00 PM at SEC 210; Thursday 5:40 PM-7:00 PM at SEC 210SEC 210 | Busch |
Historical student surveys
Teaching
3.83
Course quality
3.73
Response rate
34.7%
Coverage
3 offerings · 2020–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Matthew Stone | 1 | 4.10 | 4.10 |
| Amelie Marian | 1 | 4.20 | 3.80 |
| Jang Sun Lee | 1 | 3.20 | 3.30 |
Catalog and planning context
Credits
3
Current campuses
Busch
Current availability
0 open of 1
Catalog terms
Spring
Core codes
None listed
Loaded terms
1
(01:198:111)<em> OR </em>(01:198:142)
No direct degree-list membership appears in the checked-in requirement index.