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301117 SE Elemente des Maschinellen Lernens (2025W)
Prüfungsimmanente Lehrveranstaltung
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Details
max. 20 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
- N Montag 06.10. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 13.10. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 20.10. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 27.10. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 03.11. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 10.11. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 17.11. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 24.11. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 01.12. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 15.12. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 12.01. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 19.01. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
- Montag 26.01. 13:15 - 14:45 Seminarraum 1.4, Biologie Djerassiplatz 1, 1.013, Ebene 1
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Anyone reading this text has probably heard of or even used ChatGPT, and some have even found it rewarding. This course is not about ChatGPT, but explores the theoretical foundations of Machine Learning. We will explore basic properties of statistical models, and gain insight into the beating heart of some machine-learning algorithms. While our tools will be mathematical, the goal is not to manipulate numbers and other fancy symbols, but to gain intuition about statistical learning and the clever methods people found to drive it.We will start with basic concepts from probability theory, such as statistical independence, expectation and variance among others. The goal is to make sure we are confident in the basic building blocks that will be used throughout the course. We will then cover abstract properties like model Generalizability, VC-dimension, Bias-Variance tradeoffs, mixing them into applied algorithms like linear and logistic regression, perceptrons, and eventually neural networks to name a few.After completing the course, students will be able to apply advanced statistical tools and follow new statistical learning methods with confidence. In other words, you will be the life of every party with your incredible insights into the amazing world of machine learning theory and applications.
Art der Leistungskontrolle und erlaubte Hilfsmittel
2 quizzes and 2 homework assignments.
Homeworks contribute 50% to the final grade and quizzes contribute 50%.
A passing grade is >50% of combined assessments.
Homeworks contribute 50% to the final grade and quizzes contribute 50%.
A passing grade is >50% of combined assessments.
Mindestanforderungen und Beurteilungsmaßstab
Minimum requirements:
While the course is mathematical, I will try to be as self-contained as possible, and there are no hard minimal requirements beyond high-school level math.Assessment criteria:
Homework will involve working through mathematical problems related to course material. These will not involve a lot of elaborate algebra, but will require a deep understanding of the material.
Quizzes will be about concepts, and will not involve solving math problems.
While the course is mathematical, I will try to be as self-contained as possible, and there are no hard minimal requirements beyond high-school level math.Assessment criteria:
Homework will involve working through mathematical problems related to course material. These will not involve a lot of elaborate algebra, but will require a deep understanding of the material.
Quizzes will be about concepts, and will not involve solving math problems.
Prüfungsstoff
Students will be tested on their understanding of concepts learned during the course.
These will include the motivating ideas behind different learning algorithms, as well as theoretical analyses of learning-related probabilistic models and algorithms
These will include the motivating ideas behind different learning algorithms, as well as theoretical analyses of learning-related probabilistic models and algorithms
Literatur
None. All material will be covered in class during the lectures.
Extra reading for those who are interested will be provided in Moodle
Extra reading for those who are interested will be provided in Moodle
Zuordnung im Vorlesungsverzeichnis
MMEI II-2.2
Letzte Änderung: Fr 27.06.2025 00:02