№ files_lp_4_process_2_46147
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Academic assignment detailing a group project for a Data Science course, outlining deadlines and task requirements for student groups.
Year: 2026
Note: Region / City
Subject: Data Science / Group Project
Document Type: Academic Assignment
Author: Janet Anagli, Christoph F. Eick
Target Audience: Students
Date of Changes: Feb. 12, 2026
Deadline: March 29, 2026
Price: 8 / 10 USD
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COSC 4368: Fundamentals of Artificial Intelligence Spring 2024 Problem Set 3
Year: 2024
Region / city: Stanford University
Subject: Artificial Intelligence, Machine Learning
Document Type: Problem Set
Author: Christoph F. Eick, Raunak Sarbajna, Md/Mahin
Target Audience: Students of COSC 4368
Period of Action: Spring 2024
Submission Deadline: November 21, 11:59p
Responsible TA: Farzana Yasmin
Last Updated: September 2, 8a
Task Weight: 20% of the Problem Set Points
Dataset: Stanford Cars
Note: Learning Objectives
Review for COSC 4355 Final Exam — May 3, 2016
Date: May 3, 2016
Course: COSC 4355
Subject: Data Mining
Topics Covered: Association Rule Mining, Outlier Detection, Classification, Preprocessing, PageRank
Document Type: Exam Review
Institution: Not specified
Intended Audience: Students enrolled in COSC 4355
Question Format: Short-answer and problem-solving questions with point allocations
Algorithms Mentioned: APRIORI, Support Vector Machine, k-Nearest Neighbor, Decision Tree Induction, PageRank
Evaluation Measures: Support, Confidence, Lift, GINI, Gain Ratio, Information Gain
Source Type: Educational course material
Final Exam COSC 6342 Machine Learning, May 8, 2013
Year: 2013
Course: COSC 6342 Machine Learning
Type of Document: Exam
Format: Open book, no computers allowed
Duration: 120 minutes
Weight toward grade: 33%
Problems: 9
Topics: Information Gain, Ensemble Methods, Reinforcement Learning, Belief Networks, Kernel Methods, Support Vector Machines, K-means, Non-parametric Density Estimation
Target Audience: Graduate students in computer science
Instructor / Institution: Not specified
Clustering with K-Means and DBSCAN: COSC 4335 Assignment 2, Spring 2015
Course: COSC 4335
Institution: University-level computer science course
Instructor: Dr. Eick
Document Type: Course assignment
Subject Area: Data Mining and Clustering Algorithms
Algorithms Covered: K-means, DBSCAN
Programming Language: R
Datasets: Complex8 dataset; HAbalone dataset (modified Abalone dataset)
Original Dataset Source: UCI Machine Learning Repository Abalone Dataset
Project Type: Individual project
Learning Objectives: Clustering analysis, interpretation of clustering results, R function development, unsupervised data mining analysis, use of background knowledge in data mining
Assignment Tasks: Dataset transformation; implementation of purity evaluation function; clustering analysis with K-means and DBSCAN
Submission Deadline: March 18, 2015, 11p (early submission bonus)
Final Deadline: March 24, 2015, 11p
Document Version: Fourth Draft
Last Updated: Feb. 16, 2015, 2:30p
Topics: Task2 6335 COSC