Machine Learning Foundations ( 機器學習基石)
Course Introduction, 09/12/2017
Hsuan-Tien Lin (林軒田) [email protected]
Department of Computer Science
& Information Engineering
National Taiwan University
(國立台灣大學資訊工程系)
Four Reasons for NOT Taking the Course (1/4) Complicated Contents
• from a Taiwanese student taking MIT ML class (translated):
The professor started writing math equations as if he was using some writing accelerator. After class I always felt feeble. The worst part is: I needed to understand the contents as soon as I can. Otherwise I cannot finish the homework and cannot follow up in the next class.
• NTU ML class: designed to beas good as the best classes in the world
• similar things will happen to you
If you are not willing to be somiserable, ...
Four Reasons for NOT Taking the Course (2/4)
Strict Instructor
• Will you give me a second chance if I copy homework from other people? NO.
• Could you let me pass because I will be kicked out by the 1/2 rule?NO.
• Will you change my score from F to C? NO.
• How many will pass? Any, if necessary.
If you do not like astrict instructor, ...
Four Reasons for NOT Taking the Course (3/4) Huge Loads
• from a student taking ML 2010 (posted on BBS):
lxxxxxx9: 作業光一小題就要我們test 100次?( 100*10min = 16hr) 唉 反 覆檢查許多遍 希望是我的code寫壞了 不然出這作業的人真的很沒良 心= =
• our class: four to six times harder than a normal one in NTU
• four homework sets
• homework due within two weeks
• even have homework 0NOW
• already hard
• though no need to submit
If you do not want to spend so much time on homework, ...
Four Reasons for NOT Taking the Course (4/4)
Online-learnable
• invited by NTU as two of theMassive Online Open Courses on NTU-Coursera:Machine Learning Foundations and Machine Learning Techniques
• slides teaching
• Mandarin teaching
• Youtube-synced teaching
• “recorded” teaching mode
• mucheasier to just learn online at home
—you canchoose to only take the online version instead
If you do not want be restricted by physical class, ...
from a student in ML2013 (final feedback):
人活的好好的，為什麼一定要修Machine
Learning 呢？XD 這是一門體驗各種崩潰、絕望的課 程 ，人生能被課程電成這樣可能也就這麼一回
May the Brave Ones Stay
Basic Information
• instructor: Hsuan-Tien Lin ([email protected])
• office hour: after class or by appointment
• course webpage (CEIBA):
https://ceiba.ntu.edu.tw/1061mlfound announcements, homework, reference handouts, etc.
• mailing list: supported by CEIBA
• discussion forum: TBA
• new: two-hour credits
update your secondary email address on CEIBA
Enrollment
• With school policy, students who have taken other versions is generally NOT allowed to take this class again!
• target size 220, which is 125% of 176—assuming that 1/5 will not show up in class
• priority: CSIE (CSIE, INM) > others
• auditing: welcomed (to sit) only if there is an empty chair
Leave as soon as possible! Give your classmates a chance to be miserable.
Teaching Assistants
Boyo Chen, I-Ting Chen, Kuen-Han Tsai (tentatively)
THE Book
Learning from Data: A Short Course
Y. Abu-Mostafa (Caltech), M. Magdon-Ismail (RPI), H.-T. Lin (NTU)
• idea initiated during 2008
• 5 chapters, closely needed for this class
• teaching with the book and suggested reading within the book
Getting the Book to Read
• NTU Library: one reserved copy in the shared course material area
• R536: some shared copies to be read in the room
• Chuan-Hwa Book Company: imported some limited copies of the book
— Ms. Jen Huang ([email protected]) at 0958-008-962
— may or may not offer group discounts
• Amazon: main selling channel in the US, but can be expensive/slow for international shipping
— http://www.amazon.com/gp/product/1600490069
• Amazon Kindle: yes, butnot recommended because of its sales price
• If the book is not affordable to you but you really want to read it: email me ([email protected]) and I’ll see how I can help.
THE Principle
Taking any unfair advantages over other class members is not allowed. It is everyone’s responsibility to maximize the level of fairness.
• eating? fine, but no smells and no noise
• sleeping? fine, but no snoring
• cellphone? fine, but silent mode, and speak outside
• ...
applies to instructor, TAs, students
Honesty
NO CHEATING NO LYING NO PLAGIARISM
NO PIRATING of THE BOOK
very serious consequences
Grade
• no midterm, no final
• main reference: homework sets
• raw score goes through some order-preserving normalization steps
• raw score 80 with term rank A: possible
• raw score 80 with term rank B: possible
• raw score 60 with term rank F: possible
• raw scores 80, 60 with term scores B, B: possible, but unlikely
• raw scores 80, 60 with term scores F, B:impossible
Collaboration and Open-Book
• homework discussions: encouraged
• but fairness?
write the final solutions alone and understand them fully
• references (books, notes, Internet):
consulted, butnot copied from
• no need to lend/borrow solutions
to maximize fairness (everyone’s responsibility), lending/borrowing not allowed
Homework
• students: justify solutions clearly
• TAs: evaluate solutions fairly
• penalty for late parts:
90% of value for 12-hour late, 80% one-day late, ...
• will grant each person six penalty-free late half-days (gold medals)
late homework should go to a box in R217
Programming Assignments
• about a third or half of the problems
• any programming language, any platforms
• uploadsource code, otherwise:
10% of value only!
• no sophisticated packages
students’ responsibility:
ask TA in advance for what can/cannot be used
Important TODOs
• Update your secondary email address on CEIBA!
• Do homework 0
May the Brave Ones Stay
