Introduction

Instructor: Henry Chai Website: Here Poll: Here Piazza: Here Calendar: Here

Canvas: Here Gradescope: Here

We recommend you read these after the corresponding lecture. These readings will typically be drawn from the following texts, many of which are freely available online:

• Pattern Recognition and Machine Learning; Christopher M. Bishop.

• A Course in Machine Learning; Hal Daumé III.

• Machine Learning; Tom Mitchell.

• Machine Learning: a Probabilistic Perspective; Kevin P. Murphy.

• Dive into Deep Learning; Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola.

The textbook below is a great resource for those hoping to brush up on the prerequisite mathematics background for this course:

• Mathematics for Machine Learning, Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong.

Participation = 5%
Midterm = 25% (March 19th)
Final = 25%
6 Homework Assignments = 25%

- HW1 - HW4 = 5% Each -> 20%

- HW5 - HW6 = 2.5% Each -> 5%
Project = 20%

Your participation grade will be based on the percentage of in-class polls answered:

• 5% for 80% or greater poll participation.

• 3% for 65%-80% poll participation.

• 1% for 50%-65% poll participation.

All in-class polls will only be live until the start of the next lecture or recitation (roughly a 48-hour period); you will receive 50% credit for any poll you respond to after the corresponding lecture ends

Office Hours: 10 min each student, pseudo code

Grading: 6 grace day

• You may not use more than 2 grace days on any single assignment.

• Submissions between 0 and 24 hours late will receive a 50% multiplicative penalty.

• Submissions between 24 and 48 hours late will receive a 25% multiplicative penalty.

• Any homework submitted more than 48 hours late will receive a score of 0. Grace days may not be applied to any of the project deliverables.

Extensions: Medical Emergencies (must also CC your CMU college liaison and/or your academic advisor), Family/Personal Emergencies, University-Approved Travel (conferences, w/ confirmation of attendance), email Daniel, at least 24 hours prior to the deadline

Did you receive any help whatsoever from anyone in solving this assignment? Yes / No.
If you answered ‘yes’, give full details: ____________
(e.g., "Jane Doe explained to me what is asked in Question 3.4")
Did you give any help whatsoever to anyone in solving this assignment? Yes / No.
If you answered ‘yes’, give full details: _____________
(e.g., "I pointed Joe Smith to section 2.3 since he didn’t know how to proceed with Question 2")
Did you find or come across code that implements any part of this assignment? Yes / No. (See below policy on "found code")
If you answered ‘yes’, give full details: _____________
(book & page, URL & location within the page, etc.).

If you gave help after turning in your own assignment and/or after answering the questions above, you must update your answers before the assignment’s deadline, if necessary by emailing the course staff. Passing off any AI generated content as your own (e.g., cutting and pasting content into written assignments, or paraphrasing AI content) constitutes a violation of CMU’s Policy on Academic Integrity. "Found Code": implement from scratch without looking.

TA	Office Hour Time	Location
Lucas	Monday's 12:00pm - 1:00pm	TBA
Hadrien	Monday's 5:30pm - 6:30pm	GHC 5117
Hongwei	Tuesday's 5:30pm - 6:30pm	GHC 5117
Raghu	Wednesday's 5:30pm - 6:30pm	GHC 5117
Henry Chai	Thursday's 11:00am - 12:00pm	GHC 8133
Anna	Thursday's 5:00pm - 6:00pm	(2/1 On Zoom) Usually GHC 5117
Michael	Friday's 11:30am - 12:30pm	TBA
Karthik Balaji	Friday's 5:30pm - 6:30pm	GHC 5117