September 11
                     Lecture 1: Course Introduction
                     Lecture 2: kNN classifier

September 18
                     Lecture 3: A few Computer Vision Concepts

September 25
                     Finish Lecture 3
                     Lecture 4: Image Representations
                     Lecture 4: Curse of Dimensionality

October 2
                     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
                     Paper Discussion:
                     "Object Recognition with Informative Features and Linear Classification"
                     "Learning a Classification Model for Segmentation" Video lecture

October 22
                     Lecture 7: Support Vector Machines

October 29

November 6
                     Paper Discussion:
                     "Histograms of Oriented Gradients for Human Detection"
                     "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories"

November 13
                     Lecture 8: Bagging and Boosting

November 20
                     Paper Discussion:
                     "Rapid Object Detection using a Boosted Cascade of Simple Features"
                     "Detecting Pedestrians Using Patterns of Motion and Appearance"

November 27
                     Lecture 10: Cross Validation
                     Lecture 11: Unsupervised Learning

December 4
                     Finish Lecture 11

December 11
                     Student Paper Presentations from 9:30 to 12:30

January 17
                     Student Final Project Presentations from 10:30 to 12:00 in MC316

January 21
                     Student Final Project Presentations from 10:30 to 12:00 in MC220