Schema della sezione

• Seleziona sezione General information

  General information

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  In WS23 the course AKIDS1 will be carried out by the Chair Computer Science XII (Lehrstuhl Informatik XII): Natural Language Processing (WüNLP). The lectures will be given by Prof. Goran Glavaš, and the exercise sessions by Benedikt Ebing and Fabian Schmidt. The lectures will be primarily held in English, but the students are welcome to participate (questions and answers) in German as well; the exercises will be held in German.  

  According to the current plan and corona regulations, the lectures and exercises are in principle scheduled to take place in person (Präsenzvorlesungen). Some lectures/exercises, however, may be held online per Zoom, of which the students will be informed in a timely fashion. The kick-off session will take place in the second week of the lecture period, on Monday 23.10., in person.  

  Course Kick-off: 

  • Monday, 23.10.2022, 14:15 - 15:45  
  • Discussing the content (scope) and organization of the course.

  Lectures (two lectures weekly)

  • Monday 14:15 - 15:45 Turing-HS (Informatik)
  • Thursday 16.15 - 17.45 Turing-HS (Informatik)

  Exercises (one exercise session weekly)

  • Friday 10.15-11.45 

    SE II (Informatik)

  Winter break (no lectures and exercises): 22.12.2023 - 8.1.2024

  • Seleziona attività Ankündigungen

    

    Ankündigungen Forum
  • Seleziona attività Discussion Forum

    

    Discussion Forum
• Seleziona sezione Lectures

  Lectures

  • Seleziona attività L1: Introduction

    

    L1: Introduction File
  • Seleziona attività L2: Algorithms

    

    L2: Algorithms File
  • Seleziona attività L3: Data Structures

    

    L3: Data Structures File
  • Seleziona attività L4: Algorithmic Complexity

    

    L4: Algorithmic Complexity File
  • Seleziona attività L5: Sorting

    

    L5: Sorting File
  • Seleziona attività L6: Heap

    

    L6: Heap File
  • Seleziona attività L7: Hashing

    

    L7: Hashing File
  • Seleziona attività L8: Binary Search Tree

    

    L8: Binary Search Tree File
  • Seleziona attività L9: Balanced Trees (AVL)

    

    L9: Balanced Trees (AVL) File
  • Seleziona attività L10: Graphs

    

    L10: Graphs File
  • Seleziona attività L11: Graph Algorithms

    

    L11: Graph Algorithms File
  • Seleziona attività L12: Dynamic Programming

    

    L12: Dynamic Programming File
  • Seleziona attività L13: DP Problems

    

    L13: DP Problems File
  • Seleziona attività L14: State Space Search

    

    L14: State Space Search File
  • Seleziona attività L15: Heuristic State Space Search

    

    L15: Heuristic State Space Search File
  • Seleziona attività L16: Adversarial Search (Game Playing)

    

    L16: Adversarial Search (Game Playing) File
  • Seleziona attività L17: Metaheuristic Search (Discrete Optimization)

    

    L17: Metaheuristic Search (Discrete Optimization) File
  • Seleziona attività L18: Constraint Satisfaction and Backtracking

    

    L18: Constraint Satisfaction and Backtracking File
  • Seleziona attività L19: Knowledge-Based Reasoning (Expert Systems)

    

    L19: Knowledge-Based Reasoning (Expert Systems) File
  • Seleziona attività L20: Numeric(al) Optimization

    

    L20: Numeric(al) Optimization File
  • Seleziona attività L21: Introduction to Machine Learning

    

    L21: Introduction to Machine Learning File
  • Seleziona attività L22: Parametric Classification

    

    L22: Parametric Classification File
  • Seleziona attività L23: Non-Parametric Classification

    

    L23: Non-Parametric Classification File
• Seleziona sezione Klausur

  Klausur

  • Seleziona attività Probeklausur

    

    Probeklausur File
  • Seleziona attività Hauptklausur (13.2.): Results and Grades

    

    Hauptklausur (13.2.): Results and Grades File
  • Seleziona attività Nachholklausur (11.4.): Results and Grades

    

    Nachholklausur (11.4.): Results and Grades File
• Seleziona sezione Content

  Content

  • Seleziona attività Week 1 23.10. Lecture 1: Introduction 26.10. Lectu...

    Week 1

    • 23.10. Lecture 1: Introduction
    • 26.10. Lecture 2: Algorithms 

    Week 2

    • 30.10. Lecture 3: Data Structures
    • 02.11. Lecture 4: Algorithmic Complexity 

    Week 3

    • 6.11. Lecture 5: Sorting
    • 9.11. Lecture 6: Priority Queue and HeapSort

    Week 4 

    • 13.11. Lecture 7: Hashing
    • 16.11. Lecture 8: Binary Search Trees

    Week 5

    • 20.11. Lecture 9: Balanced Trees 
    • 23.11. Lecture 10: Graphs 

    Week 6: 

    • 27.11. Lecture 11: Graph Algorithms
    • 30.11. Lecture 12: Dynamic Programming

    Week 7

    • 04.12. Lecture 13: DP Examples
    • 07.12. Lecture 14: (Uninformed) State Space Search Heuristic

     

    Semester break

    Week 8

    • 08.01. Lecture 15: State Space Search (A* Algorithm)
    • 11.01. Lecture 16: Adversarial State Space Search (Minimax)

    Week 9
    

    • 15.01. Lecture 17: Metaheuristic Search
    • 18.01. Lecture 18: Constraint Satisfaction (Backtracking)

    Week 10

    • 22.01. Lecture 19: Expert Systems
    • 25.01. Lecture 20: Numerical Optimization

    Week 11

    • 29.01. Lecture 21: Introduction to Machine Learning
    • 25.01. Lecture 22: Parametric Classification

    Week 12 

    • 05.02. Lecture 23: Non-Parametric Classification
    • 08.02. Lecture 24: Probeklausur
• Seleziona sezione Exercises

  Exercises

  • Seleziona attività Exercise 0 - Introduction to Python

    

    Exercise 0 - Introduction to Python Cartella
  • Seleziona attività Exercise 01 - Stack & Queue

    

    Exercise 01 - Stack & Queue Compito
  • Seleziona attività Exercise 01 - Stack & Queue - Solutions

    

    Exercise 01 - Stack & Queue - Solutions File
  • Seleziona attività Exercise 02 - Complexity & Sorting

    

    Exercise 02 - Complexity & Sorting Compito
  • Seleziona attività Exercise 02 - Complexity & Sorting - Solutions

    

    Exercise 02 - Complexity & Sorting - Solutions Cartella
  • Seleziona attività Exercise 03 - Heaps & Hashing

    

    Exercise 03 - Heaps & Hashing Compito
  • Seleziona attività Exercise 03 - Heaps & Hashing - Solutions

    

    Exercise 03 - Heaps & Hashing - Solutions File
  • Seleziona attività Exercise 04 - Binary & Balanced Trees

    

    Exercise 04 - Binary & Balanced Trees Compito
  • Seleziona attività Exercise 04 - Binary & Balanced Trees - Solutions

    

    Exercise 04 - Binary & Balanced Trees - Solutions File
  • Seleziona attività Exercise 05 - Graphs

    

    Exercise 05 - Graphs Compito
  • Seleziona attività Exercise 05 - Graphs - Solution

    

    Exercise 05 - Graphs - Solution Cartella
  • Seleziona attività Exercise 06 - Dynamic Programming

    

    Exercise 06 - Dynamic Programming Compito
  • Seleziona attività Exercise 06 - Dynamic Programming - Solutions

    

    Exercise 06 - Dynamic Programming - Solutions File
  • Seleziona attività Exercise 07 - State Space Search

    

    Exercise 07 - State Space Search Compito
  • Seleziona attività Exercise 07 - State Space Search - Solution

    

    Exercise 07 - State Space Search - Solution Cartella
  • Seleziona attività Exercise 08 - Adversarial Search

    

    Exercise 08 - Adversarial Search Compito
  • Seleziona attività Exercise 08 - Adversarial Search - Solution

    

    Exercise 08 - Adversarial Search - Solution File
  • Seleziona attività Exercise 09 - Metaheuristic & Constrained Satisfaction

    

    Exercise 09 - Metaheuristic & Constrained Satisfaction Compito
  • Seleziona attività Exercise 09 - Constrained Satisfaction - Solution

    

    Exercise 09 - Constrained Satisfaction - Solution Cartella
  • Seleziona attività Exercise 10 - Expert Systems & Numerical Optimization

    

    Exercise 10 - Expert Systems & Numerical Optimization Compito
  • Seleziona attività Exercise 10 - Expert Systems & Numerical Optimization (Solution)

    

    Exercise 10 - Expert Systems & Numerical Optimization (Solution) Cartella
  • Seleziona attività Exercise 11 - Machine Learning

    

    Exercise 11 - Machine Learning Compito