ACIT4510 Statistical Learning Emneplan

The course covers the foundations and recent advances in Machine Learning from the point of view of Statistical Learning Theory. The goal of this course is to provide students with the practical skills to support the theoretical knowledge acquired during the lecture course and the practical intuitions needed to use and develop effective machine learning solutions to challenging problems.

Access to good statistical/data analysis software is paramount. Therefore, we will illustrate the use of the models throughout the course with real implementation.

The following required coursework must be approved before the student can take the exam:

Four assignments in groups of 1 - 3 students (1000 - 2000 words per assignment) ;;;;

No formal requirements over and above the admission requirements.

The student should have the following outcomes upon completing the course:

Knowledge

Upon successful completion of the course, the student:

• will have a good understanding the different concepts and methods of supervised and unsupervised statistical learning and how to apply them on large data.
• has advanced knowledge of probabilistic formulation of the various learning problems

;

Skills

Upon successful completion of the course, the student:

• can apply different high-dimensional regression techniques on data
• can apply different classification techniques on data
• can apply clustering techniques on data
• can derive learning algorithms for new models and analyze new data with them.
• can apply dimensionality reduction techniques on data

;

General competence

Upon successful completion of the course, the student:

• can apply different predictive models on data and assess their performance
• can use supervised and unsupervised learning in different real life problem

This course is divided into two parts. The first part with focus on covering the principles of Statistical Learning. Different seminars will be given on the different methodological aspects of Statistical learning, mainly, supervised learning and unsupervised learning.

The second part will focus on the students completing a programming project. This is a real data analysis problem, where the student is asked to carry out the analysis using the tools and techniques from the course and hand in a report documenting the steps he has taken in the analysis. The ultimate goal is to build a predictive model.

The project report will consist of at least 25 pages and max 60 pages.

During this part, there may be lectures if needed, but most of the time will be spent on individual supervision of students in lab-sessions.

Practical training

Lab sessions.

The following required coursework must be approved before the student can take the exam:

One mandatory assignment: A project plan document containing a description of the chosen data set, a preliminary research question and suggested tools and method to apply.

An individual project report approximately 2500 - 5000 words, excluding appendixes.

The exam can be appealed,

;

New/postponed exam

In case of failed exam or legal absence, the student may apply for a new or postponed exam. New or postponed exams are offered within a reasonable time span following the regular exam. The student is responsible for applying for a new/postponed exam within the time limits set by OsloMet. The Regulations for new or postponed examinations are available in Regulations relating to studies and examinations at OsloMet.

This course provides a hands-on overview of common theories and methods used in the design of robotic and autonomous systems. The course is organized around weekly practical labs and lectures that complement each other. The student will get hands-on experience with the technologies, algorithms,;and architecture of robotic and autonomous systems. The course uses examples from aerial, space, ground, underwater, and industrial robotic and autonomous systems.;

No formal requirements over and above the admission requirements.;

The student should have the following outcomes;upon completing the course:;

Knowledge:;

Upon successful completion of the course, the student should:;

• have advanced knowledge on robotic and autonomous systems components and architecture;
• have advanced knowledge in modeling kinematics and dynamics of robotic;systems;
• have advanced knowledge in common sensor and actuator technologies used in robotics;
• have advanced;knowledge of algorithms and methods used in state estimation, navigation, and motion planning
• have a good understanding of the Robot Operating System (ROS) and software architectures used in robotic and autonomous;systems;

Skills:;

Upon successful completion of the course, the student:;

• can analyze a robotic and autonomous systems;with regard to;its components, architecture, and their;purpose;
• can model and analyze kinematic and dynamics of robotic;systems;
• can apply;a number of;algorithms and methods in state estimation,;navigation, and motion planning
• can analyze and implement solutions based on Robot Operating System;(ROS);

General competence:;

Upon successful completion of the course, the student:;

• can discuss the role of robotic and autonomous systems in;a number of;practical;applications;
• can analyze how robotic and autonomous systems operate and design specific components using ROS and other software;tools.;

This course will feature weekly lectures and lab work to provide both theoretical and hands- on experience. Students will work in groups and complete assignments given to them. The student will supplement the lectures and lab with their own reading. The students will;also work on a individual project.;