CS680:
Special Topics in Computer Science
Advances
in Image Understanding and Machine Learning
Spring 2014
Berrin Yanıkoğlu,
Hakan Erdoğan
Brief Description
The objective in this course is to follow
recent developments in computer vision and machine learning; especially
relating to object detection and recognition, image retrieval and related
techniques in machine learning. In particular, we will look into the topic of
deep learning in depth, as well as topic models, word embeddings, new object
representations, and possibly structured, online, semi-supervised learning
methods etc. according to interest.
We are going to read scientific papers (about
4-6 per week) at varying depth (learn state-of-the-art over some application
papers versus understand this method in sufficient detail) and follow tutorials
(about 3-4 tutorials during the course).
The class will be participatory with students
and faculty members presenting/leading papers/tutorials in turn. Each student
will also be expected to implement a new technique on a simple/toy problem
(e.g. deep learning on a simple problem), as well as working towards on one of
the assigned current problems (e.g. in parallel with a large scale object
recognition or retrieval competition).
There will be no exams, grading will be done
according to class participation/contribution (60%) and homework/project (40%),
with roughly the given weights.
Prerequisite: Machine
Learning (CS516) and/or Pattern Recognition (EE566).
Intended Audience: Advanced graduate students (2nd
year MS or PHD students).
Tentative
Grading: Course participation (attendance and
contribution): 20% Presentations:
40% Hws/Project:40%.
Course
participation is measured through students’ involvement in the discussion about
the presented material.
Presentation
grade will be given for student presentation of a chosen paper
describing some state-of-the-art approach.
Project will
involve some implementation and/or improvement of an existing research. It can
be done in groups of 2, and
Ideally it
would result in a conference paper or a technical report.
Schedule
and Reading Materials:
We will start with http://ufldl.stanford.edu/wiki/index.php/UFLDL_Tutorial
The
tutorial presentation will be mixed with presentations of papers describing
state-of-the-art work in large scale object recognition and in particular those
using deep
learning. For instance http://www.image-net.org/challenges/LSVRC/2013/iccv2013.php
At least
the initial few presentations will be mostly at overview level.
http://deeplearning.net/reading-list/tutorials/