Course Details

  • Instructor Profile: Gunawardena, Ananda "Andy"
  • Prerequisite Information:

    01:198:205.

    - 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
  • Topics:

    - 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).