CS 178: Machine Learning Fall 2017

Created by Sameer Singh, sameersingh.org
Instructor Prof. Sameer Singh
Office Hours DBH 4204 Office Hours
Lectures DBH 1100 TuTh 3:30-4:50
Discussions DBH 1500 Wed 12-12:50p,
DBH 1500 Wed 1-1:50p,
SE2 1304 Wed 3-3:50p,
HIB 110 Wed 4-4:50p
Teaching Assistants TBA
Readers TBA
Course Code 34240
Other Links Piazza, Gradescope, EEE

Description coming soon.
Prerequisites
At minimum:
  • ICS 6B: Boolean Algebra and Logic
  • ICS 6D: Discrete Mathematics for Computer Science
  • ICS 6N or MATH 3A: Linear Algebra
  • MATH 2B: Single-Variable Calculus
  • STATS 67 or (STATS 7 and STATS 120A): Introduction to Probability and Statistics
  • Programming assignments will require a working familiarity with Python, along with familiarity with data structures and algorithms.
Contact me if you are concerned about your background for the course.
Grading Policy
  • Exams: 50%
    • Mid-Term: 20%
    • Final: 30%
  • Assignments: 25%
    • 5 assignments
  • Quizzes and Polls: 10%
    • During discussions
  • Project: 10%
  • Piazza Participation: 5%
Piazza
We will be using Piazza for class discussion. The system is highly catered to getting you help fast and efficiently from classmates and myself. Rather than emailing questions to me, I encourage you to post your questions on Piazza. If you have any problems or feedback for the developers, email team@piazza.com.

Find our class page at: https://piazza.com/uci/fall2017/cs178/home

Late Submission Policy
The late submission policy for this course is fairly straightforward: you get 5 grace days to use towards the individual assignments (there is no grace day for the project submission). There will be no questions asked, you can use these days as you see fit. However, if you run out of grace days, and still submit late, your submission will not be graded and you will get a 0 for that submission (and no excuse will be entertained). Any leeway in this policy will only be entertain if pre-arranged (before submission) with the instructor, and under extenuating circumstances.
Academic Honesty
Academic honesty is a requirement for passing this class. Any student who compromises the academic integrity of this course is subject to a failing grade. The work you submit must be your own. Academic dishonesty includes, but is not limited to copying answers from another student, allowing another student to copy your answers, communicating exam answers to other students during an exam, attempting to use notes or other aids during an exam, or tampering with an exam after it has been corrected and then returning it for more credit. If you do so, you will be in violation of the UCI Policies on Academic Honesty (see link). It is your responsibility to read and understand these policies. Note that any instance of academic dishonesty will be reported to the Academic Integrity Administrative Office for disciplinary action and may be cause for a failing grade in the course.