Dr. John Rachlin

Associate Teaching Professor


E-mail j.rachlin@northeastern.edu
Office Hours (Sp25) TBD on Zoom
By appointment only.

Teaching

I teach and develop online courses from my home in Flagstaff, Arizona.



Computer Science
CS 1800: Discrete Structures
CS 1802: Recitation for CS 1800
CS 3200: Introduction to Databases
CS 5002: Discrete Structures
CS 5200: Database Systems

Data Science
DS 2000: Programming with Data
DS 2001: Practicum for DS 2000
DS 2500: Intermediate Programming with Data
DS 2501: Lab for DS 2500
DS 3000: Foundations of Data Science
DS 3500: Advanced Programming with Data
DS 4300: Large-Scale Storage and Retrieval
DS 4400: Machine Learning and Data Mining 1
DS 4973: Topics in Data Science - Astronomical Data Mining
DS 4973: Topics in Data Science - Society of Mind: AI for Humans
DS 4992: Directed Study - Collaborative Research Projects
DS 5110: Data Management and Processing

Proposed Courses (in Development):
DS XXXX: Scientific Computing
DS XXXX: Nature-Inspired Computing
DS XXXX: Data Science Applications in Archaeology


NEW COURSE!
DS 4973: Society of Mind: AI for Humans (Fall 2024)



This course offers a comprehensive introduction to Artificial Intelligence (AI) designed for non-computer science majors. No programming experience is required. Students will explore the history and major milestones of AI, gaining insight into the technologies that are rapidly reshaping industries, enhancing human intelligence, and sparking new avenues of creativity. Key concepts such as deep learning, natural language processing, recommendation systems, and generative AI will be demystified through real-world examples. The course will also delve deeply into the legal, ethical, and societal implications of AI, equipping students with the skills needed to effectively engage with modern AI technologies. Through discussions, case studies, and interactive activities, students will develop a nuanced understanding of AI’s strengths and limitations, preparing them to make informed decisions about the use of AI in their personal and professional lives.

Prerequisites: None

Website: SOMAI


NEW COURSE!
DS 4973: Astronomical Data Mining



Astronomical surveys including the Sloan Digital Sky Survey, Pan-STARRS, Gaia, and the Transiting Exoplanet Survey Satellite (TESS) have produced vast quantities of data in the form of images and object catalogs. In the past decade, Astronomical Data Mining (or Astroinformatics) has become an important interdisciplinary field of research. This course is designed to introduce students to the principles and practices of this emerging topic, equipping them with the data science skills necessary to analyze, interpret, visualize, and make discoveries with astronomical datasets. Through weekly research paper reviews and programming assignments, students will learn the fundamentals of astronomical data mining, gain hands-on experience with current astronomical catalogs, and review case studies of major discoveries made through data-intensive astronomical research. Prerequisites: Advanced Python Programming, Database Systems.