Introduction to Machine Learning
(Fall 2024)

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Announcements for Week 11 (Mon, Nov 18 - Fri, Nov 22)

  • Heads up: Drop Date is Wednesday, November 20, 2024.

  • This week follows a normal schedule.

  • The 6.390 final exam will be held on Monday, December 20, 2024, from 9:00 AM to 12:00 PM Eastern Time, at Johnson Track. Please be sure to review final exam logistics page (for info about deadline for requesting conflict accommodations, and practice finals).

Midterm Results

  • The midterm grades have been released on Gradescope. You will need to log in with your MIT email for Gradescope access (this applies to cross-registered students as well).

  • Due to the difficulty of the exam, we have applied a flat offset of +14 points to every submission. The median score is 79.5, the mean is 80.18, and the standard deviation is 14.08.

  • Midterm solutions can be found here. A blank midterm can be found here.

  • Regrade requests need to be made through Gradescope. You must include a clear statement and justification for reconsideration for specific question(s) and part(s) that you seek a regrade on. We are suspectible to human error; we want to make sure everyone obtains all of the points that they have earned.

  • Our grading review in response to a regrade request can result in no change, addition of points, or reduction of points; we may also review grading on the rest of your midterm to correct for other grading mistakes, if any.

  • Requests for midterm regrades will open on Monday, October 28 at 9am, and close on Wednesday, October 30 at 11pm. Please take the time between now and Monday to thoroughly review your exam. You may consult with the course staff on Piazza or in Office Hours if you are unsure whether or not you should request a regrade. We will not respond to regrade requests in Gradescope until the window has closed.

  • Your progress page will reflect your midterm exam score once regrades are complete.

  • Regular class meetings and office hours resume next week on Monday, October 28.

The calendar below will be populated throughout the semester with links to notes, lectures, recitations, labs, and homeworks. The current week is highlighted vertically in blue.

Topic 1: Intro to ML

First class: Lab on Wed, 4 Sep
Notes:  Course Overview Slides, Chap. 1, Chap. 2 up to 2.4
No Exercises
No Recitation
Lab: in class
Homework: online
Topic 2: Analytical Regression

Notes:  Chap. 2, Appendix A - Matrix derivatives
Lecture: in class
   Start: Fri, 6 Sep at 12:00pm
Exercises: online
Recitation: in class
   Start: Mon, 9 Sep at 9:30am
Lab: in class
Homework: online
Topic 3: Gradient Descent

Notes:  Chap. 3
Lecture: in class
   Start: Fri, 13 Sep at 12:00pm
Exercises: online
Recitation: in class
   Start: Mon, 16 Sep at 9:30am
Lab: in class
Homework: online
Topic 4: Linear Classifiers and Logistic Regression

Fri, 20 Sep (Student holiday)
Notes:  Chap. 4
Lecture: in class
   Start: Fri, 20 Sep at 12:00pm
Exercises: online
Recitation: in class
   Start: Mon, 23 Sep at 9:30am
Lab: in class
Homework: online
Topic 5: Features

Add Date: Fri, 4 Oct
Notes:  Chap. 5
Lecture: in class
   Start: Fri, 27 Sep at 12:00pm
Exercises: online
Recitation: in class
   Start: Mon, 30 Sep at 9:30am
Lab: in class
Homework: online
Topic 6: Neural Networks

Notes:  Chap. 6 up to 6.5
Lecture: in class
   Start: Fri, 4 Oct at 12:00pm
Exercises: online
Recitation: in class
   Start: Mon, 7 Oct at 9:30am
Lab: in class
Homework: online
Topic 7: Neural Networks II, Autoencoders

No class on Mon, 14 Oct (Indigenous Peoples' Day)
Notes:  Chap. 6 to end, Chap. 10
Lecture: in class
   Start: Fri, 11 Oct at 12:00pm
Exercises: online
No Recitation
Lab: in class
Homework: online
Midterm Exam Week (Mon, 21 Oct - Fri, 25 Oct)

Midterm Exam: Wed, 23 Oct at 7:30pm
Review Session: in class
  Start: Fri, 18 Oct at 12:00pm, 45-230
No Recitation on Mon, 21 Oct
No Lab on Wed, 23 Oct
Topic 8: Convolutional Neural Networks

Notes:  Chap. 8
Lecture: in class
   Start: Fri, 25 Oct at 12:00pm
Exercises: online
Recitation: in class
   Start: Mon, 28 Oct at 9:30am
Lab: in class
Homework: online
Topic 9: Transformers

Notes:  Chap. 9
Lecture: in class
   Start: Fri, 1 Nov at 12:00pm
Exercises: online
Recitation: in class
   Start: Mon, 4 Nov at 9:30am
Lab: in class
Homework: online
Topic 10: Clustering

No class on Mon, 11 Nov (Veterans Day)
Notes:  Chap. 7
Lecture: in class
   Start: Fri, 8 Nov at 12:00pm
Exercises: online
   Released: Wed, 6 Nov at 5:00pm
   Due: Wed, 13 Nov at 9:00am
No Recitation
Lab: in class
   Released: Wed, 13 Nov at 9:30am
   Due: Mon, 18 Nov at 11:00pm
Homework: online
   Released: Mon, 11 Nov at 9:00am
   Due: Wed, 20 Nov at 11:00pm
Topic 11: Markov Decision Processes

Notes:  Chap. 11
Lecture: in class
   Start: Fri, 15 Nov at 12:00pm
Exercises: online
   Released: Wed, 13 Nov at 5:00pm
   Due: Mon, 18 Nov at 9:00am
Recitation: in class
   Start: Mon, 18 Nov at 9:30am
Lab: in class
   Released: Wed, 20 Nov at 9:30am
   Due: Mon, 25 Nov at 11:00pm
Homework: online
   Released: Mon, 18 Nov at 9:00am
   Due: Wed, 27 Nov at 11:00pm
Topic 12: Reinforcement Learning

Notes:  Chap. 12
Lecture: in class
   Start: Fri, 22 Nov at 12:00pm
Exercises: online
   Released: Wed, 20 Nov at 5:00pm
   Due: Mon, 25 Nov at 9:00am
Recitation: in class
   Start: Mon, 25 Nov at 9:30am
Lab: in class
   Released: Wed, 27 Nov at 9:30am
   Due: Mon, 2 Dec at 11:00pm
Homework: online
   Released: Mon, 25 Nov at 9:00am
   Due: Wed, 4 Dec at 11:00pm
Topic 13: Decision Trees and Nearest Neighbors

Guest Speaker for Lecture on Fri, 6 Dec; Last Due Date: Fri, 6 Dec
Notes:  Chap. 13
Lecture: in class
   Start: Fri, 29 Nov at 12:00pm
Exercises: online
   Released: Wed, 27 Nov at 5:00pm
   Due: Mon, 2 Dec at 9:00am
Recitation: in class
   Start: Mon, 2 Dec at 9:30am
Lab: in class
   Released: Wed, 4 Dec at 9:30am
   Due: Fri, 6 Dec at 11:59pm
No Homework
Topic 14: End of term wrapup

Topic TBD
Notes:  TBD
No Exercises
No Recitation
No Lab
No Homework
Final Exam Week (Mon, 16 Dec - Fri, 20 Dec)

Final Exam: Fri, Dec 20 at 9:00am
Information and Practice Materials


Batch download/materials: Complete Lecture Notes Draft - Note that chapters are subject to change week by week.