jlotzer
  • SMLInformatik IInformatik II
    • SMLInformatik IInformatik II




    Informatik II

    This section contains the weekly exercises and teaching materials for the lecture Computer Science II. If the downloads are not working, simply click here , log in, and reload the page.

    • Summary

      This summary was written by Daniel Steinhauser and me. No guarantee can be given for correctness and completeness. English version here

      PDF Download

    • Polybox

      Here you'll find my Polybox (Passwort: hier klicken) containing old exams, summaries and notes of many subjects:

      Polybox

    • Google Colab

      Google Colab is a very useful tool for using Jupyter Notebooks online. No installation is required, as everything runs in the cloud.

      Google Colab




    Week 1

    Einführung Python, Jupyterlab installieren. Allgemeine Tipps und Informationen

    • Einführung
    • Python
    • Jupyter


    Week 2

    Python Containers (Ranges, Slicing), Exceptions, List & Dict Comprehensions

    • Containers
    • Exceptions
    • List
    • Dict


    Week 3

    List & Dict Recap, Aliasing, Recursion, Debugging

    • Aliasing
    • Recursion
    • Debugging


      Week 4

      Aliasing Recap, Numpy, Matplotlib, Pandas

      • Numpy
      • Matplotlib
      • Pandas


        Week 5

        Algorithmen, Asymptotic Notation, Laufzeitanalyse, Insertion Sort

        • Big O
        • Asymptotics
        • Insertion Sort


        Week 6

        Recursive Runtime Analysis Trick - No telescoping needed

        • Master Theorem


          Week 7

          Trees, Binary Search Trees, Tree Traversal, Min/Max-Heap

          • Trees
          • BST
          • Heaps


          Week 8

          Ordered & Unordered Datastructures, Hash Table, Quadtrees

          • BBST
          • Hash
          • Quadtree


            Week 9

            Programming Concepts, Functional/Object-Oriented Programming

            • Terminology
            • Lambdas
            • Classes


              Week 10

              Dynamic Programming I

              • Bottom up
              • DP Table
              • Optimal Substructure


                Week 11

                Dynamic Programming II

                • Bottom up
                • DP Table
                • Optimal Substructure


                  Week 12

                  Machine Learning I

                  • Machine Learning
                  • Decision Trees
                  • Linear Regression


                    Week 13

                    Mock Exam. Slides und Mitschrift (bereitgestellt von Alexander Liemen):

                    • Mock Exam


                    Week 14

                    Machine Learning II

                    • Neural Networks
                    • Hyperparameters
                    • Cross Validation

                      2024 Julian Lotzer