Algorithms and data structures
DE
Self-study
Algorithms and data structures

Algorithms and data structures

University of Innsbruck

Justus Piater

About

Duration 14 units
Unit 2 hours/unit
Licence CC BY-NC-SA 4.0
Participants 390
Availability 3 November 2024
Start Date 4 March 2024
Costs € 0.00

Content

Course Content

Algorithms and Data Structures is a free MOOC (Massive Open Online Course) for computer science students and everyone interested in the topic. 


The videos teach basic concepts about abstract data types, data structures and algorithms, which are reinforced via exercises and quizzes. We provide a script that includes the contents shown in the videos as well as supplementary material.

The course is split into the following 14 units :

  1. Introduction
  2. Analysis of algorithms
  3. Recursion
  4. Stacks and queues
  5. List abstractions
  6. Trees
  7. Priority queues
  8. Maps
  9. String search
  10. Search trees
  11. Greedy algorithms
  12. Divide and conquer
  13. Dynamic programming
  14. Graphs

Course Goals

The course teaches basic concepts in the following areas:

  • abstract data types, data structures, algorithms
  • complexity analysis (asymptotic resource analysis) of algorithms
  • basic, versatile data structures and algorithms
  • understanding and application of the algorithmic paradigms of greedy algorithms, divide and conquer and dynamic programming
  • sorting algorithms

Certificate

For actively participating in the course you will receive an automatic certificate which includes your username, the course name as well as the completed lessons. We want to point out that this certificate merely confirms that the user answered at least 75% of the self-assessment questions correctly.

Licence

This work is licensed under CC BY-NC-SA 4.0

Course Instructor

Justus Piater
Justus Piater

Univ.-Prof. Justus Piater, Ph.D. works at the Institute for Computer Science at the University of Innsbruck and heads the Intelligent and Interactive Systems research group. In his research, he seeks to enable autonomous robots to perceive and act flexibly and robustly in unstructured environments, leveraging machine learning methods to build perceptual, motor and reasoning skills. Main applications are sensor-based grasping and manipulation of objects and learning of motion sequences.

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Partners

University of Innsbruck

University of Innsbruck

Innsbruck

2617 Participants
8 Courses
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