Instructor: Srikumar Ramalingam
Lecture Time: Mon,Wed 1:25PM-2:45PM
Place: WEB L114
TA: Xin Yu (xin.yu@utah.edu)
Date | Lecture Number | Topic |
1/7 | L1 | Introduction to Computer Vision |
1/9 | L2 | Camera Models and Image Formation (Final) |
1/14 | L3 | Camera Pose Estimation and RANSAC (Final) |
1/16 | L4 | Class canceled & extra TA hours |
1/21 | Martin Luther King Jr. Day | |
1/23 | L5 | Image Matching (Final) ( Paper, Vocabulary Tree Example) |
1/28 | L6 | Motion Estimation (Final) |
1/30 | L7 | 3D Reconstruction (Final) |
2/4 | L8 | Introduction to Graphical Models (Final) Slides |
2/6 | L9 | Introduction to Graphical Models (Final) |
2/11 | L10 | Belief Propagation (Final) |
2/13 | L11 | Stereo Matching |
2/18 | Presidents' Day | |
2/20 | L12 | Project Discussions |
2/25 | L13 | Graph Cuts (Final) |
2/27 | L14 | Graph Cuts (continued) |
3/4 | L15 | Graph Cuts (continued) |
3/6 | L16 | Object Detection |
3/11 | *Spring Break* | |
3/13 | *Spring Break* | |
3/18 | L17 | Using neural nets to recognize handwritten digits (Final) ( Slides , Chapter1 ) |
3/20 | L18 | Using neural nets ... (continued) |
3/25 | L19 | Project Discussions and Feedback |
3/27 | L20 | How the backpropagation works (Final) |
4/1 | L21 | How the backpropagation works ( Slides ,Chapter2) |
4/3 | L22 | Improving the way the network learns ( Slides |
4/8 | L23 | Convolutional Neural Networks ( Slides ) |
4/10 | L24 | Overview of Gradient Descent Algorithms ( Slides ) |
4/15 | L25 | Review (Pose Estimation, 3D Recon, Image Matching, Belief propagation) ( Practice Questions with answers ) |
4/17 | L26 | Review (Graph Cuts & Deep Learning) ( Practice Questions with Answers ) |
4/24 | Final Exam (1:00 - 3:00 PM) | |
4/30 | 1 - 3 PM Final Project Presentations (5 + 2 minutes for each group) | |