Gathering of Lecture Exercises
Hi there! Velkommen til my collection of the exercises at DTU.
| Exercise |
Topic |
| ex1 |
Digital Images |
| ex2 |
Cameras |
| ex3 |
Pixelwise Operations |
| ex4 |
Filtering |
| ex5 |
Morphology |
| ex6 |
Blob Analysis and Cell Counting |
| ex7 |
Pixel Classifcation |
| ex9 |
Geometric Transformations |
| ex10 |
Registration |
| ex11 |
Line and Path Tracing |
| exam |
|
| Exercise |
Topic |
| week1 |
Rigid and Perspective Transformations in Homogeneous Coordinates |
| week2 |
Camera Model |
| week3 |
Multiview Geometry |
| week4 |
Linear Algorithms |
| week5 |
Camera Calibration |
| week6 |
Simple Features |
| week7 |
ORB Features |
| week8 |
Structured Light |
| Exercise |
Topic |
| week1 |
Motor |
| week2 |
Robotics Kinematics 1 |
| week3 |
Robotics Kinematics 2 |
| week4 |
Velocity Kinematics |
| week6 |
Trajectory Planning and Dynamics |
| week9 |
Control an Industrial Robot |
| final assignment |
Alto Robot Analysis |
| exam |
|
31388 Advanced Autonomous Robot
| Exercise |
Topic |
| ex1 |
Introduction |
| ex2 |
Laser |
| ex3 |
Kinematics |
| ex4 |
Motion Control |
| ex5 |
Vision |
| ex8-11 |
Extended Kalman Filter |
| ex12-13 |
Path Planner |
31390 Unmanned Autonomous Systems
| Exercise |
Topic |
| part1 |
Rotation |
| part2 |
Modeling |
| part3 |
Control |
| part4 |
Path Planning |
| part5 |
Trajectory Planning |
| part6 |
Simulation |
31391 Software Framework for Autonomous Robots
| Exercise |
Topic |
| week1 |
Software Architecture |
| week2 |
Middleware for AS |
| week3 |
TF, RVIZ and Gazebo |
| week4 |
Robot Kinematics, Motion Planning and Execution |
| week5-6 |
Motion Planning |
| week 7 |
Autonomous Guided Vehicle |
| week 10 |
Final Assignment |
31392 Perception for Autonomous System
| Exercise |
Topic |
| week1 |
Image Processing |
| week2 |
Image Feature Description and Matching |
| week3 |
Multiple View Geometry 1 |
| week4 |
Multiple View Geometry 2 |
| week5 |
3D Point Cloud Processing 1 |
| week6 |
3D Point Cloud Processing 2 |
| week7 |
State Estimation |
| week8 |
Classification |
| week9 |
Visual Odometry |
02450 Introduction to Machine Learning and Data Fitting
| Exercise |
Topic |
| week1 |
Introduction |
| week2 |
Data, feature extraction and PCA |
| week3 |
Measures of similarity, summary statistics and probabilities |
| week4 |
Probability densities and data visualization |
| week5 |
Decision trees and linear regression |
| week6 |
Overfitting, cross-validation and Nearest Neighbor |
| week7 |
Performance evaluation, Bayes, and Naive Bayes |
| week8 |
Artificial Neural Networks and Bias/Variance |
| week9 |
AUC and ensemble methods |
| week10 |
K-means and hierarchical clustering |
| week11 |
Mixture models and density estimation |
| week12 |
Association mining |
| Exercise |
Topic |
| week1 |
Feedforward network 1 |
| week2 |
Feedforward network 2 |
| week3 |
Feedforward network 3 |
| week4 |
Convolutional neural network |
| week5 |
Recurrent neural network |
| week6 |
Mini projekt |
| week7 |
Unsupervised learning |
| week8 |
Reinforcement learning |
02610 Optimization and Data Fitting
02807 Computational Tools for Data Science