KUSS: 033 536 Artificial Intelligence
Curriculum
Recommended:
Below, italic means NOT according to this recommendation, i.e. collapsed from a future semester.
This is my plan for if I don’t get a better opp inbetween, where I’d either default to recommended or drop.
S = signed up, D = done / completed (successfully), F = failed / open
Status 2026-09-08
64 ECTS done (26.5 accredited). Open from year 1: Math II VL (exam 29 Sep, Wien), Math II UE (failed, repeat SS 2027), Stats VL + UE (exams 30 Oct, Linz HS 1). Registration tooling:
kusss --help, planning pageagent/show/jku-ws2026/index.html, skill.claude/skills/kusss.
Started deviating from thsi plan bcs no point in finishing 1-2 sems earlier. More time to soak things up, side-projects, internships, work, …
Sem 1 (W):
| Course | Type | ECTS | Groups / Times | Link | S | D |
|---|---|---|---|---|---|---|
| Introduction to AI | VL | 3.0 | — | kusss | x | x |
| Hands-on AI 1 | VL | 1.5 | — | kusss | x | x |
| Hands-on AI 1 | UE | 1.5 | Mon, 15:30-17:00, 14T | kusss | x | x |
| Current Topics in AI | KV | 1.5 | — | kusss | x | x |
| Mathematics for AI | VL | 6.0 | — | kusss | x | x |
| Mathematics for AI | UE | 3.0 | Thu, 13:45-15:15 | kusss | x | x |
| Logic | VL | 3.0 | — | kuss | x | x |
| Logic | UE | 1.5 | Mon, 19:00-19:45 | kuss | x | x |
| Algorithms & Datastructures 2 | VO | 3.0 | — | kusss | x | x |
| Algorithms & Datastructures 2 | UE | 1.5 | Thu, 15:30-17:30, 14T | kusss | x | x |
| Machine Learning: Supervised Techniques | VL | 3.0 | — | kusss | d | |
| Machine Learning: Supervised Techniques | UE | 1.5 | Thu, ??-??, 1.5h, 14T | kusss | d |
ECTS = 27 [21 ECTS (core - accredited) + 9 6 ECTS from 3rd semester]
Sem 2 (S):
| Course | Type | ECTS | Groups / Times | Link | S | D |
|---|---|---|---|---|---|---|
| Hands-on AI II | VL | 1.5 | — | x | x | |
| Hands-on AI II | UE | 3.0 | x | x | ||
| Programming in Python II | VL | 1.5 | — | x | x | |
| Programming in Python II | UE | 1.5 | x | x | ||
| Statistics for AI | VL | 3.0 | — | x | F | |
| Statistics for AI | UE | 3.0 | x | F | ||
| Machine Learning and Pattern Classification | VL | 3.0 | — | x | x | |
| Mathematics for AI II | VL | 6.0 | — | x | F | |
| Mathematics for AI II | UE | 3.0 | x | F | ||
| Machine Learning: Unsupervised Techniques | VL | 3.0 | — | d | ||
| Machine Learning: Unsupervised Techniques | UE | 1.5 | d | |||
| Machine Learning and Pattern Classification | UE | 1.5 | x | x | ||
| Computational Data Analytics (Fürnkranz) | KV | 3.0 | d | |||
| Learning from User-generated Data | VL | 3.0 | — | d | ||
| Learning from User-generated Data | UE | 1.5 | d | |||
| Visualization | VL | 3.0 | — | x | F | |
| Visualization (no online?) | UE | 1.5 | d |
43.5 ECTS [25.5 ECTS (core - accredited) + 18 ECTS from 4th semester]
Sem 3 (W), WS 2026:
Curriculum drift vs. the 2025 PDF
Responsible AI is VL 1.5 + UE 1.5 now (not offered WS 2026). “Introduction to Computational Statistics” became “Statistical Machine Learning” (Avalos Pacheco, Präsenz-only UE). Both ML institute lectures (ML Basic Techniques, ML Supervised VL) filled past 500 seats on registration day; one mail to office@ai-lab.jku.at got both assigned the next morning.
| Course | Type | ECTS | Groups / Times | Link | S | D |
|---|---|---|---|---|---|---|
| Artificial Intelligence | VO | 3.0 | Mon 12:00, streamed (344.014) | kusss | x | |
| Artificial Intelligence | UE | 1.5 | ranked 344.221 Thu 09:15 DL, 344.117 Mon 15:30 DL (Reihenfolge) | kusss | x | |
| Mathematics for AI III | VL | 6.0 | Tue + Wed 10:15, streamed (357.260) | kusss | x | |
| Mathematics for AI III | UE | 3.0 | 531.949 async remote group (quizzes + substitute exam) | kusss | x | |
| Reinforcement Learning | VL | 3.0 | Thu 13:45, streamed (344.156) | kusss | x | |
| Reinforcement Learning | UE | 1.5 | ranked 344.125 Thu 17:15 DL, 344.228 Thu 17:15 hybrid, 344.123 Fri 11:00 DL | kusss | x | |
| Machine Learning: Supervised Techniques | UE | 1.5 | ranked 365.304, 365.303 (Thu 10:15 DL), 365.098 (12:00), 365.306, 365.305 (08:30) | kusss | x | |
| Machine Learning: Supervised Techniques | VL | 3.0 | Wed 15:30, streamed (365.075), late registration via AI office | kusss | x | |
| Machine Learning: Basic Techniques | KV | 3.0 | Thu 15:30, streamed (365.074), late registration via AI office | kusss | x | |
| Computational Logics for AI | VL | 3.0 | skipped this year (Logic cost 13 h/ECTS) | |||
| Computational Logics for AI | UE | 1.5 | ||||
| Natural Language Processing | VL | 1.5 | not taken | |||
| Natural Language Processing | UE | 1.5 | ||||
| Statistical Machine Learning | VL | 3.0 | not taken (R vs Python unclear, UE Linz-only) | |||
| Statistical Machine Learning | UE | 1.5 | ||||
| Practical Work in AI | PR | 7.5 | not taken (needs supervisor; only remote group 3× oversubscribed) |
Exams registered: Math II VL Tue 29 Sep 15:30 Wien · Stats VL Fri 30 Oct 12:00 Linz HS 1 · Stats UE Fri 30 Oct 14:45 Linz HS 1.
Allocation for the ranked UEs runs after 25 Sep 23:59. Studienausmaß cap set to 30 ECTS (Teilzeitbonus 1). Abmeldefrist: VLs until course start (5/8 Oct); UEs count attempts, drop before their deadline.
Registered: 25.5 ECTS + 12 ECTS exams from year 1 (37.5 if everything passes → 101.5 done; 78.5 left: ~3 more semesters at curriculum pace, 4 at ~20/semester)
Sem 4 (S):
Considerations for free electives and area of specialization / special topics
Maybe i can take this masters course as free lective etc?
[ 993MLPEMLTV25 ] VL (*)Machine Learning: Advanced Techniques
Area of specialization:
[ INBIPVOBEKO ] VL Berechenbarkeit und Komplexität(mildy interesting but good prof; same for quantum computing one)
993MLPEDATV25Diffusion models etc.
[Deep Learning: Geometric Techniques](https://studienhandbuch.jku.at/185894)
[ 201ANLSGD1V18 ] VL Ordinary differential equations and dynamical systemsmaybe, but rather steve
[ 201WTMSMACV22 ] VL Markov Chainsmaybe
[ 201NUOPOPTV18 ] VL Optimizationmaybe but rather steve; or combine
[ 993TALSBSAV25 ] VL Biological Sequence Analysisa bit of biology; good instructor; but not sur eif this specific topic is that interesting
Make sure I cover: statistical mechanics, dynamical systems, nonlinear dynamics, information theory, renormalization
| Course | Type | ECTS | Groups / Times | Link |
|---|---|---|---|---|
| Seminar in AI | SE | 3.0 | ||
| Responsible AI | KV | 3.0 | ||
| Numerical Optimization | VL | 3.0 | — | |
| Numerical Optimization | UE | 1.5 | ||
| Area of Specialization | — | 7.5 | ||
| Area of Specialization | — | 3.0 | ||
| Area of Specialization | — | 1.5 | ||
| Free electives | — | 0.5 | ||
| Gender Studies | KV | 3.0 | ||
| Digital Signal Processing | VL | 3.0 | — | |
| Digital Signal Processing | UE | 1.5 | ||
| Bachelor’s Thesis Seminar in AI | SE | 9.0 |
39.5 ECTS [18.5 ECTS (core - 2nd sem - accredited) + 16.5 ECTS from 6th semester + 3 ECTS from 5h + 1.5 ECTS from 4th]
Accredited courses:
| Course | Type | ECTS |
|---|---|---|
| Programming in Python 1 | — | 6.0 |
| Technology and Society | KV | 3.0 |
| Formal Models in AI | — | 4.5 |
| Algorithms and Data Structures 1 | — | 4.5 |
| Free Electives | — | 8.5 |
| Total | 26.5 |
https://chatgpt.com/c/68bf4d05-9fc0-8333-abaa-6ffca1e93fdb table formating fun; 5 is good
https://chatgpt.com/c/68bf5ed4-a144-8323-9b5f-47778eaf77ae schedule plot