6.390 Intro to

Machine Learning

Course Structural Overview 

Shen Shen

December 9, 2022

Syllabus

Info/prereqs

Course Pedagogy

A nominal week: mix of theory, concepts, and application to problems!

Grading: Exercises: 5%, Attendance: 5%, Homeworks: 15%,Lab Checkoff: 15%, Midterm: 25%, Final: 35%

- Reading Notes: all the "facts" and "points" we expect
- Exercises: easy questions based on notes (and optional viewing of previous recorded lecture)
- Recitation: In-class pen/paper problems; assumes you have read and done exercises; start on homework
- Homework: (9-days) Harder questions: concepts, mechanics, implementations
- Lab: In-class empirical exploration of concepts; work with partner on lab assignment; Check-off conversation with staff member

- Office hours (18 hours) and Piazza help

Student Body

Some Policy "Quirks"

  • Infinite submissions
  • No sick attendence
  • 20-day auto extensions

Staff/management

Vision/Evolution

Kick-off point ML family tree

What's brewing

  • Course materials by topic (meta tags)
  • Collaborative notes-reading
  • One week dedicated to "what's trendy/latest"?
  • Resources Sharing
    • Problems bank; tags/filters
    • Piazza FAQs bank
    • Staff handbooks
    • Centralized personnel db (e.g. we benefitted from joint LA hiring)

6.390 overview

By Shen Shen

6.390 overview

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