Junkun Chen (email@example.com); office hours: W/F 5-6pm on his zoom.
Ning Dai (firstname.lastname@example.org); office hours: M 5-6pm on the above zoom.
Instructor office hours available the weeks before HWs are due (usually 3pm Tuesdays); on the same zoom.
CS: algorithms and datastructures. fluent in at least one mainstream languages (Python, C/C++, Java).
HWs will be done in Python+numpy only.
Math: linear algebra, calculus, and basic probability theory. geometric intuitions.
Bishop (2007). Pattern Recognition and Machine Learning (PRML). Actually I do not recommend it for beginners. But the figures are pretty and I use them in my slides.
Background survey (on Canvas):
each student gets 2% by submitting on time.
Quizzes (on Canvas, autograded): 10% + 8% = 18%. everybody has two attempts on each quiz.
HWs 1-4 (programming): 20% + 15% + 15% + 15% = 65%.
In Python+numpy only, on a Unix-like environment (Linux or Mac OS X).
Windows is not supported or recommended. IDEs are not necessary either.
HW5: Paper review: 15%. cutting-edge machine learning research.
HWs are generally due on Mondays; Quizzes are generally due on Fridays.
Late Penalty: Each student can be late by 24 hours only once without penalty. No more late submissions will be accepted.
We use three tools for communication:
Course homepage: textbooks, handouts, slides, ipython notebooks and demo programs, lecture videos, homework, and data.
Canvas: announcements (you'll receive emails), homework submission, (autograded) quizzes and surveys, and grades.
Slack: discussions. Please post all course-related questions on Slack so that the whole class may benefit from our conversation.
Please contact us privately only for matters of a personal nature. As a strictly enforced course policy, we will not reply to any technical questions via email.
Machine Learning evolves around a central question:
How can we make computers to
learn from experience and without being explicitly programmed?
In the past decade, machine learning has given us
practical speech recognition,
effective web search,
accurate spam filters,
and a vastly improved understanding of the human genome.
Machine learning is so pervasive today that everybody uses it
dozens of times a day without knowing it.
This course will survey the most important algorithms and techniques
in the field of machine learning.
The treatment of Math will be rigorous,
but unlike most other machine learning courses
tons of equations,
my version will focus on the geometric intuitions
and the algorithmic perspective.
I will try my best to visualize every concept.
Even though machine learning appears to be "mathy" on the surface,
it is not abstract in any sense,
unlike mainstream CS (algorithms, theory, programming languages, etc.).
In fact, machine learning is so applied and empirical
that it is more like alchemy.
So we will also discuss practical issues and
Some preparatory materials:
Quiz 3 (perceptron) HW2 out [tex]
(same data as HW1)
Perceptron Extensions; Perceptron in Practice
1. Python demo
2. Perceptron Extensions: voted and averaged (§4.6)
3. MIRA and aggressive MIRA (not in CIML)
4. Practical Issues (§5.1-5.4)
5. Perceptron vs. Logistic Regression (§9.6)
Aizerman et al 1964. Theoretical foundations of the potential function method in pattern recognition learning. (translated from Russian, in the same journal) (origin of kernels and kernelized perceptron)