- Course Number: 01:198:439
- Instructor: Ananda Gunawardane
- Course Type: Undergraduate
- Semester 1: Spring
- Credits: 4
This course covers topics needed to solve problems involving data, which includes preparation (collection and integration), characterization and presentation (information visualization), analysis (machine learning and data mining), and products (applications).
- Syllabus: https://go.rutgers.edu/35ra8ico
- Video Intro: https://www.youtube.com/watch?v=xytdWiBFHaI&feature=youtu.be
- Instructor Profile: Gunawardena, Ananda "Andy"
- Prerequisite Information:
- A grade below a "C" in a prerequisite course will not satisfy that prerequisite requirement.
- Course Links: 01:198:205 - Introduction to Discrete Structures I
- Data visualization
- Data wrangling and pre-processing
- Map-reduce and the new software stack
- Data mining: finding similar items, mining data streams, frequent itemsets, link analysis, mining graph data
- Machine learning: k nearest neighbor, decision trees, naive Bayes, regression, ensemble methods, support vector machines, k-means, spectral clustering, hierarchical clustering, dimensionality reduction, evaluation techniques
- Applications: recommendation systems, advertising on the Web
- Expected Work: Homework assignments and a semester-long project
- Exams: Midterm and final exams
- Learning Goals:
Computer Science majors ...
- will be prepared to contribute to a rapidly changing field by acquiring a thorough grounding in the core principles and foundations of computer science (e.g., techniques of program design, creation, and testing; key aspects of computer hardware; algorithmic principles).
- will acquire a deeper understanding on (elective) topics of more specialized interest, and be able to critically review, assess, and communicate current developments in the field.
- will be prepared for the next step in their careers, for example, by having done a research project (for those headed to graduate school), a programming project (for those going into the software industry), or some sort of business plan (for those going into startups).