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| /* More information can be found on [[https://isis.tu-berlin.de/course/view.php?id=17150| ISIS]]. */ | /* More information can be found on [[https://isis.tu-berlin.de/course/view.php?id=29810| ISIS]]. */ |
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| Attendance is not mandatory. The structure is roughly given below: | The weekly structure is roughly given below: || Tue 10:15 - 11:45 am || Lecture || || Thu 10:15 - 11:45 am || Exercise || |
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|| 10:00 – 11:30 am || Introductory lecture || || 11:30 – 3:00 pm || Work on exercise sheets || || 3:00 – 4:00 pm || Review of exercise sheets || || 4:00 – 5:00 pm || Work on homework sheets || |
Exercise sheets will be collected in the lecture. |
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| * Week 5 - Online Test |
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| '''Registration is desired until 15.05. but necessary to attend the course.''', students of all fields and universities are invited. /* Basis for passing the course is a test (90 minutes). Prerequisite for the participation in the test is the achievement of at least half of all possible points in the homework, the results in the exercises are not included in the grade. */ | '''Registration is desired until 15.05. but necessary to attend the course''', students of all fields and universities are invited. The decision wether the course will take place in person or online, depends on the number of attendees, and will be announced about around the 23.05./* Basis for passing the course is a test (90 minutes). Prerequisite for the participation in the test is the achievement of at least half of all possible points in the homework, the results in the exercises are not included in the grade. */ |
Mathematical Foundations for Machine Learning
Lecture period:
30.05. - 01.07.
Contact:
Thomas Schnake (t.schnake@tu-berlin.de)
ISIS Course:
Information
The goal of this course is to freshen and deepen the mathematical foundations from the computer science program that are necessary for the lectures Cognitive Algorithms and Machine Learning.
Topics of the course come from analysis (differentiation), linear algebra (vector spaces, dot products, orthogonal vectors, matrices as linear maps, determinants, eigenvalues and eigenvectors) and probability theory (multivariate probability distributions, calculations with expectation values and variances).
Structure
The weekly structure is roughly given below:
Tue 10:15 - 11:45 am
Lecture
Thu 10:15 - 11:45 am
Exercise
Exercise sheets will be collected in the lecture.
Preliminary structure:
- Week 1 - Linear Algebra I: Groups, Fields and Euclidean Vector Spaces
- Week 2 - Linear algebra II: Linear Transformations, Matrices and Determinants
- Week 3 - Analysis: Differentiation and ML Examples
- Week 4 - Probability Theory
- Week 5 - Online Test
Credits
The course is part of the module Machine Learning 1-X (M.Sc. Informatik) and optional for Cognitive Algorithms (B.Sc. Informatik).
Registration is desired until 15.05. but necessary to attend the course, students of all fields and universities are invited. The decision wether the course will take place in person or online, depends on the number of attendees, and will be announced about around the 23.05./* Basis for passing the course is a test (90 minutes). Prerequisite for the participation in the test is the achievement of at least half of all possible points in the homework, the results in the exercises are not included in the grade. */