CSCI-UA 473: Fundamentals of Machine Learning
An introductory course in statistical machine learning covering foundational algorithms and mathematical principles. Students implement and evaluate linear regression, logistic regression, support vector machines, decision trees, ensemble methods, k-means…
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Common aliases: CSCI-UA 473
Study Resources for Fundamentals of Machine Learning
Midterm (Exam 1) 2016
Midterm (Exam 2) 2013
Final Exam 2011
Midterm (Exam 1) 2017
Midterm (Exam 1) 2018
Midterm (Exam 1) 2019
Midterm (Exam 1) 2020
Worksheet 1
Assessment Inventory for CSCI-UA 473 (8 Documents)
| Document | Format | Year | Access |
|---|---|---|---|
| Midterm (Exam 1) 2020 | Midterm | 2020 | Illustrative Archive |
| Midterm (Exam 1) 2019 | Midterm | 2019 | Illustrative Archive |
| Midterm (Exam 1) 2018 | Midterm | 2018 | Illustrative Archive |
| Midterm (Exam 1) 2017 | Midterm | 2017 | Illustrative Archive |
| Midterm (Exam 1) 2016 | Midterm | 2016 | Illustrative Archive |
| Midterm (Exam 2) 2013 | Midterm | 2013 | Illustrative Archive |
| Final Exam 2011 | Final | 2011 | Illustrative Archive |
| Worksheet 1 | Worksheet | — | Illustrative Archive |
Complete inventory of all 8 assessments (sorted by most recent). Select any document to inspect or use the search bar above to filter.
Course Overview & Exam Syllabus
Course Description
An introductory course in statistical machine learning covering foundational algorithms and mathematical principles. Students implement and evaluate linear regression, logistic regression, support vector machines, decision trees, ensemble methods, k-means clustering, PCA, and neural networks, balancing theoretical derivations with practical Python implementations.
This archive catalogs 8 free educational resources and past exams for CSCI-UA 473 (Fundamentals of Machine Learning) at New York University (NYU), containing 6 midterm papers, 1 final examination, and 1 worksheet. Dated documents represent 7 distinct academic years between 2011 and 2020.
Core Exam Topics & Learning Objectives
- Supervised learning algorithms: linear/logistic regression, decision trees, SVMs, and ensemble methods in CSCI-UA 473
- Neural network architectures: backpropagation calculus, activation functions, convolutional layers, and regularization
- State-space search and decision theory: A* heuristic search, adversarial minimax, and Markov Decision Processes
- Model evaluation metrics: cross-validation, precision/recall trade-offs, ROC-AUC, and bias-variance tradeoff in Fundamentals of Machine Learning
Frequently Asked Questions
Which assessment formats are cataloged in this CSCI-UA 473 archive?
This collection for CSCI-UA 473 at New York University (NYU) catalogs 8 total resources across the following formats: Midterms, Finals, Worksheets. Each document is listed in the assessment inventory table with its individual format tag and academic year where recorded.
What academic years are covered in this CSCI-UA 473 collection?
Dated materials in this archive span from 2011 to 2020, representing 7 distinct academic years. Coverage varies by the records currently cataloged for this course.
How can students access these free CSCI-UA 473 past exams?
All cataloged CSCI-UA 473 past exams and study materials are free to request. The catalog, metadata, assessment inventory, and illustrative thumbnails are public; source documents are not shown on this page. Use the connect options to request a study resource, and confirm current course topics and grading policies with the New York University (NYU) course syllabus.