Login
Machine Learning for Robotics and Computer Vision (IN3200) (2h + 1h, 4ECTS)
SS 2017, TU München
Lecture
Location: Room MW HS0250
Date: Friday
Time: 10.15 - 12.00
Lecturer: PD Dr. habil. Rudolph Triebel
ECTS: 4
SWS: 3
Tutorial
Location: Room 02.09.023
Date: every second Tuesday starting from May 16th
Time: 14.00 - 16.00
Lecturer: John Chiotellis
Contents
In this lecture, the students will be introduced into the most frequently used machine learning methods in computer vision and robotics applications. The major aim of the lecture is to obtain a broad overview of existing methods, and to understand their motivations and main ideas in the context of computer vision and pattern recognition.
Tentative Schedule:
- Introduction
- Regression
- Probabilistic Graphical Models
- Metric Learning
- Boosting
- Neural Networks and Deep Learning
- Kernel Methods
- Gaussian Processes
- Evaluation and Model Selection
- Sampling Methods
- Clustering
Lecture Slides
1. Introduction and Probabilistic Reasoning
2. Regression and Maximum Likelihood Estimation
3. MAP Regression and Kernel Methods
4. Gaussian Processes for Regression and Classification
5. Sparse Gaussian Processes / Clustering
6. Metric Learning
7. Boosting
8. Bagging / Sequential Data
9. Neural Networks and Deep Learning
10. Hidden Markov Models / Sampling I
11. Sampling II / Variational Inference I
12. Variational Inference II
13. Online Learning
Exercises
0. Linear Algebra Refresher
Linear Algebra Exercises
1. Probabilistic Reasoning
2. Regression, Kernels, GP
train_data.txt
3. Laplace Approx., EM, k-means
clustering.zip
4. Metric Learning, Boosting
banknote_auth.zip
5. Neural Networks and Deep Learning
6. Hidden Markov Models, Sampling
Exam Preparation
To prepare for the exam you can be helped by studying the questions here. Note that these are questions from oral exams of previous semesters and do not necessarily cover all topics covered in the current semester.
Exam Information
No assisting material is allowed, like calculators, formula sheets, etc.
Please note that there will be no repeat exam, since the lecture is offered in every semester.
Date: August 18th
Time: 8:30 - 10:00
Location: 102, Interims Hörsaal 2 (5620.01.102)
Exam Review
Please bring your TUM ID card with you.
Date: September 15th
Time: 10:00 - 11:00
Location: Room 02.09.023
