Florian Vogt

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I am enough of an artist to draw freely upon my imagination. Imagination is more important than knowledge. Knowledge is limited. Imagination encircles the world.

Albert Einstein

About me

  • 💼 Student @ ETH Zürich
  • 🧑‍🎓 BSc Mechanical Engineering @ D-MAVT
  • 📌 Switzerland 🇨🇭 / Germany 🇩🇪
  • ⌛ 20 y/o
  • 🗜️ Maker / 3D printing
  • 🎾 Tennis
  • 🗳️ Politics

Curious about the world and passionate about solving real-world challenges, I’m a mechanical engineering student exploring how technology and hands-on projects can shape a better future. I’m excited to learn, grow, and make an impact along the way.

Activity

Last 365 days

Last update:

Informatik I

Alle Übungsunterlagen meiner Informatik I Übungsstunde (Donnerstag, 14:15-16:00) im HS 25.

Aktueller Raum: CHN D 46

Woche Themen
1 [RS/FF] Introduction, First C++ Program
2 [FF] Expressions, Fundamental Types
3 [FF] Control Flow I
4 [RS] Control Flow II, Functions I
5 [RS] Functions II
6 [FF] Reference Types, Vectors
7 [FF] Strings, Recursion I
8 [RS] Number Representations
9 [RS] Recursion II, Custom Data Types
10 [FF] Information Hiding, Iterators and Containers
11 [FF] Dynamic Memory, Pointers, Dynamic Data Structures I
12 [RS] Memory Management with Classes
13 [RS] Dynamic Data Structures II
14 [RS/FF] Subtyping, Polymorphism and Inheritance

Informatik II

Alle Übungsunterlagen meiner Informatik II Übungsstunde (Mittwoch, 16:15-18:00) im FS 26.

Aktueller Raum: CAB G 59

Achtung: Der Kurs wurde im FS 26 um ein paar Themen (z.B. Quadtrees) erweitert. Die Zusammenfassung müsste daher ergänzt werden.

Woche Themen
1 [MF] Introduction: Python Containers (Ranges, Slicing)
2 [MF] More Python: Containers continued (List Comprehension, Dictionary Comprehension) and numpy
3 [MF] Python libraries: Matplotlib, pandas
4 [MF] Python Classes, Programming concepts: functional, static vs. dynamic, interpreted, etc.
5 [MF] Algorithm design: Asymptotics, running time, design techniques
6 [MF] Searching and sorting: Binary search, recursive analysis, quicksort
7 [MF] Trees: search trees, heaps
8 [CC] Data structures: efficiency comparisons, Quadtrees
9 [CC] Dynamic programming: Recursion and memoization
10 [CC] Dynamic programming: Optimal substructures
11 [CC] Intro to ML I:
  • decision trees, linear regression, logistic regression
  • training, validation
12 Ascension: no lecture, exercises as normal
13 [CC] Intro to ML II:
  • Non-numeric attributes
  • Model complexity and Overfitting
  • Cross-validation
  • Neural networks and deep learning
14 [CC] Intro to ML III: unsupervised learning
  • supervised, unsupervised, reinforcement learning
  • Clustering
  • Dimensionalitätsreduktion