CS 178: Machine Learning Fall 2017
| 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 |
| Other Links | Piazza, Canvas, Gradescope |
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:
- 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.
- Grading Policy
-
- Exams: 50%
- Mid-Term: 20%
- Final: 30%
- Assignments: 30%
- 5 assignments (drop lowest grade)
- Project: 15%
- Participation: 5%
- Polls/Quizzes
- Piazza Interactions
- Exams: 50%
- 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 a total of 5 grace days to use across all the individual assignments, with a maximum of 3 days for a single assignment (there is no grace day for the project submission). There will be no questions asked, you can use these days as you see fit under these conditions. However, if you run out of grace days and still submit late, or are later than the maximum, your submission will not be graded and you will get a 0 for that submission (and no excuse will be entertained).
- 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.