September 10
Lecture 1: Course Introduction
Lecture 2: kNN classifier
September 17
Finish Lecture 2
Lecture 3: A few Computer Vision Concepts
September 24
Finish Lecture 3
Lecture 4: Image Representations
Lecture 5: Curse of Dimensionality
October 1
Paper Discussion:
"Recognizing Action at a Distance"
"80 million tiny images: a large dataset for non-parametric object and scene recognition"
October 8
Lecture 6: Linear and Generalized Linear Classifiers
October 15
Finish Lecture 6 and:
Paper Discussion:
"Learning a Classification Model for Segmentation" Video lecture
October 22
Lecture 7: Support Vector Machines
October 29
Paper Discussion:
"Histograms of Oriented Gradients for Human Detection"
"Beyond Bags of Features: Spatial Pyramid Matching
for Recognizing Natural Scene Categories"
November 5
Lecture 8: Bagging and Boosting
November 12
Paper Discussion:
"Rapid Object Detection using a Boosted Cascade of Simple Features"
"Detecting Pedestrians Using Patterns of Motion and Appearance"
November 19
Lecture 9: Cross
Validation
Lecture 10: Neural Networks
November 26
Finish Lecture 10 and
Paper Discussion:
"ImageNet Classification with Deep Convolutional
Neural Networks"
December 2
"Student Paper Presentations from 11:30 to 13:30 in MC320"
December 3
"Student Paper Presentations from 10:30 to 12:30"
December 4
"Student Paper Presentations from 10:30 to 12:30 in MC320"
January 20
Student Final Project Presentations from 10:00 to 12:00 in MC300
January 20
Student Final Project Presentations from 1:30 to 3:30 in MC300
January 21
Student Final Project Presentations from 10:00 to 12:00 in MC316