# Evaluation Criteria AI Bachelor >**NOT EVERYTHING IN THIS DOC WILL BE CORRECT BECAUSE THINGS CHANGE EVERY SEMESTER. I HAVE ONLY CREATED IT SO THAT YOU CAN IMAGINE WHAT TO EXPECT. CONTRIBUTE IF YOU HAVE DONE THE COURSES RECENTLY OR HAVE DONE THE ONES WHERE NOTHING IS WRITTEN YET. THANKS! - ABDUL B.** ## 1st Semester (WS): ### Logic VL+UE StEOP: YES Grading: * weekly minitests during the winter semester * optionally supplemented by lab exercises * if passed positively, no further exam is required * alternative: one big exam * over whole content of the course (lecture and exercises) In either case, you get two certificates (with the samegrade): one for the lecture and one for the exercises Minitests: * each week * duration: 15 minutes * each handed-in test is worth up to 5 points * a handed-in test is positive with ≥ 2.5 points * up to 1 additional point can be earned by solving the weekly challenges * no test can be repeated or taken at a later time VL: no mandatory attendance for minitest YES --- ### Hands on AI I StEOP: YES Lecture grading: * There will be 1-2 online tests about the key ideas of the lecture * Grades are made up by the 2 multiple-choice tests. * In order to pass the LVA, more than 50% of all points (at least 40% in each test) are required. Retry test: * voluntary retry test * Topics: everything that was covered in the lectures * The test will replace the worse of the previous tests Exercise grading: * 7 exercises * Grading involves the 7 exercises. VL and UE: no mandatory attendance --- ### Introduction to AI StEOP: YES * Written Exam * no mandatory attendance --- ### Lecture Series Artificial Intelligence StEOP: NO * For each talk there is an online assignment in Moodle. * There is no exam beyond these assignments. * Grading is based only on the assignments. * We have two weeks time to hand in a plain text file (.txt) with 200 to 400 words length. * File = Summary with main message of the talk * no mandatory attendance --- ### Responsible AI StEOP: NO * Total points that can be achieved in the course: 50: * Group exercise: max. 20 points (at least 11 points must be achieved, otherwise repeat/fail) * Written exam: max. 26 points (at least 14 points must be achieved, otherwise repeat/fail) * 5 Mini tasks voluntary: 1 point each (if completed within 2 weeks after announcement!) * no mandatory attendance --- ### Programming in Python I StEOP: YES * There are 3 assignments and 2 small online multiple-choice exams. * Lecture and exercise share the same grade. * The main part (80%) of the grade are determined by the assignments. * A small part (20%) of the grade are determined by the 2 exams. * A total score of 50% on the exams (points exam1 + points exam2) is required to pass the course. * Assignments and online multiple-choice exams are available via moodle. --- ### Mathematics for AI I **Mario Ullrich:** VL+UE: Final written exam * Weekly exercises have to be solved and uploaded on Moodle (at least 50% of the total number of exercises have to be uploaded to be allowed to take part in the final written exam for the exercises) --- ## 2nd Semester (SS): ### Hands-on AI II StEOP: YES VL: * -2 multiple-choice exams. * min. 40% of the points on each exam. * min. 50% of the points on both combined exams. UE: * Grading based on 7 assignments --- ### Technology and Society StEOP: NO * 50% Participation * Giving an oral presentation in a group of 3 students by presenting an additional reading-text of about 15 to 20 minutes, including (1) a presentation of the core arguments, (2) linking the text to the core reading, and (3) relating the text to AI. OR * Individually writing 2 reading diaries of about 1 page each related to a core reading of your choosing, including (1) a summary of the core message and/or arguments of the text, and (2) two to three questions for further discussion. The reading diaries are to be uploaded on Moodle until Sunday evening before the respective session. * 50% Final exam * 45-minutes online open-book exam via Moodle, whereas students are required to answer two out of three open questions. * In order to pass this course, both elements (participation and exam) have to be covered! * Attendance is recommended, but not controlled! Note, however, that recordings are only provided for selected parts of the class. --- ### Algorithms and Data Structures 1 StEOP: YES VL: Written Exam UE: * 5 of 7 assignments have to be submitted. A assignment with less than 6 points achieved is condidered as not submitted. * For a positive grade 50% of the total score of 5 best submissions have to be achieved. --- ### Programming in Python II StEOP: YES * 90% of the grade is based on the project submission, 10% of the grade is based on a short online multiple-choice exam. * The exam has to be positive (>=50% on exam). * VL and UE share the same grade. --- ### Statistics for AI StEOP: NO VL: * positive lecture grade = positive exam (i.e. at least 50 points) * exam (total 100 points): * examples (e.g. calculate some summary statistics for given data) * theory questions (e.g. explain the principal idea of a statistical test) * open book exam (i.e. any printed paper documents) * no electronical devices (no cell phone, no tablet, no computer, …) * except a „real“ calculator" * no mandatory attendance * 2022S: https://www.jku.at/en/department-of-applied-systems-research-and-statistics/teaching/courses/summer-semester/ UE: * Presence Course: * each week students present homework examples * grade = performance of these presentations + total number of prepared examples * at least 60% of the total homework examples are marked as „solved & prepared“ * 5 presentations of homework examples and 60% of these presentations are positive * each part is weighted with 50% and has to be positive, if you are „between grades“ * the presentations decide your grade * no exam * Distance Learning Course: * solving of homework examples (= identical to the presence course) * grade = number of correctly solved examples + written exam * 2022S: https://www.jku.at/en/department-of-applied-systems-research-and-statistics/teaching/courses/summer-semester/ --- ### Mathematics for AI II **Georg Regensburger & Thibaut Verron SS21:** VL: Final written exam UE: * Weekly attendance (online) is mandatory * Weekly exercises have to be solved and uploaded on Moodle (at least 50% of the total number of exercises have to be uploaded to be allowed to take part in the final written exam for the exercises) * Presence (online, max 3 absences during the semester, inform your instructor in advance) * Presentation of solutions of the exercises during weekly Zoom classes * 3 short Moodle online tests (The best two tests count and you need more than 50% of the points in order to be allowed to participate at the final written exam for the exercises. The results of the online tests will also be taken into account for the final grade.) * • Final grade: 50% final exam, 20% moodle tests, 20% number of crosses, 10% presentation **Mario Ullrich:** VL+UE: Final written exam * Weekly exercises have to be solved and uploaded on Moodle (at least 50% of the total number of exercises have to be uploaded to be allowed to take part in the final written exam for the exercises) --- ## 3rd Semester (WS): ### Artificial Intelligence VL: final exam UE: * Graded assignments * Bonus points from the voluntary competition * No exam --- ### Algorithms and Data Structures 2 VL:final exam Allowed material: Printed/written documents such as slides and supplementary material that has been presented in lecture and exercise. Also the digital PDF versions may be used. UE: ![](https://i.imgur.com/JmtkeMr.png) --- ### Basic Methods of Data Analysis KV: final exam --- ### Visual Analytics VL: final exam UE: Individual and group assignments. --- ### Machine Learning: Supervised Techniques VL: final exam UE: Graded assignments and Exam --- ### Mathematics for AI III VL: final exam UE: Final grade: 50% final exam, 20% moodle tests, 20% number of crosses, 10% presentation Mario: only one exam for VL and UE --- ## 4th Semester (SS) ### Seminar in AI Writing and presenting a “survey article” --- ### Computational Data Analytics final exam --- ### Learning from User-generated Data VL: final exam UE: Graded assignments --- ### Formal Models VL + UE: * Five tests * if passed positively, no further exam is required * five regular tests * positive presentation * Big exam * over whole content of the course (lecture and exercises) * at the end of SS In either case, you get two certificates (with the same grade): one for the lecture and one for the exercises --- ### Machine Learning: Unsupervised Techniques VL: final exam UE: Graded assignments + positive presentation and final exam --- ### Machine Learning and Pattern Classification VL: final exam UE: practical project to be carried out (in groups) during the semester. --- ### Numerical Optimization VL: Practical projects to be carried out in groups or Exam UE: Graded assignments --- ## 5th Semester (WS) ### Practical Work in AI (7.5 PR) Depends on where you do it. Everyone does it alone. Mostly it is the practical part of your thesis. --- ### Introduction to Computational Statistics VL: project in groups of 4 UE: Homework with 4 examples --- ### Natural Language Processing VL: - final exam - Multiple-choice, fill the blank, etc. (MOODLE) UE: - Assignments are done in groups of two persons - Each assignment offers extra points - Extra points only cover any missing points in the assignment - Total points do not exceed the maximum points of the assignment --- ### Computational Logics for AI Grading will depend on the results of online quizzes during the semester (40% of total points) and the final written exam (60% of total points). You will get the same grade for the lecture and the exercises. To pass, you need to collect more than 50% of the total points. --- ### Reinforcement Learning The lecture and the exercise are separate. To pass the lecture, you need to pass the exam. To pass the exercise, you need to have more than 50% of all points. --- ## 6th Semester (SS) ### Digital Signal Processing VL: final exam UE: group assignments