CS 273A: Machine Learning Fall 2018

• About
• Schedule
• Assignments
• Projects
• Resources

How can a machine learn from experience, to become better at a given task? How can we automatically extract knowledge or make sense of massive quantities of data? These are the fundamental questions of machine learning. Machine learning and data mining algorithms use techniques from statistics, optimization, and computer science to create automated systems which can sift through large volumes of data at high speed to make predictions or decisions without human intervention.

Machine learning as a field is now incredibly pervasive, with applications from the web (search, advertisements, and suggestions) to national security, from analyzing biochemical interactions to traffic and emissions to astrophysics. Perhaps most famously, the $1M Netflix prize stirred up interest in learning algorithms in professionals, students, and hobbyists alike; now, websites like Kaggle host regular open competitions on many companies' data.

This class will familiarize you with a broad cross-section of models and algorithms for machine learning, and prepare you for research or industry application of machine learning techniques.

Prerequisites

At minimum:

• CS 271: Introduction to Artificial Intelligence (or equivalent)
• CS 206: Principles of Scientific Computing (or equivalent)
• 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: 30%
  • 5 assignments (drop lowest grade)
• Project: 15%
• Participation: 5%
  • Polls/Quizzes/Evaluation
  • Campuswire Participation

Campuswire

This term we'll be using Campuswire for all class Q&A, announcements, and discussions. I'll post all class announcements on Campuswire and you should use it to ask any class-related questions. The staff and I will check Campuswire frequently and answer unresolved questions, but you're also encouraged to collaborate with each other and answer each other’s questions.

We're using Campuswire for all class announcements, q&a and discussions. Please do not email me with questions except for official matters — paste them on Campuswire.

Find our class page at (email me for the code): https://campuswire.com/p/GAF58E3D6.

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, with a maximum of three towards any one assignment. 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 entertained if pre-arranged (before submission) with the instructor, and under extenuating circumstances (i.e., assume I will not grant it).

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 Policy on Academic Honesty and the ICS Policy on Academic Honesty). 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.