News
Tutorial assignmentWritten on 26.10.26 by Elina Rojin Celik Dear students, you can now find your assigned tutorial on your Personal Status page. All tutorials take place in E1 3 HS001. Tutorials start this week and will cover Problems 1 and 2 from Exercise Sheet 1, which you can find in the Materials section. Have a nice week!
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Materials + Logistics update (we start always at 4pm)Written on 15.10.26 by Sara Magliacane Hi all, Just a few announcements for the materials and logistics: - I have uploaded the slides and the videos of the first lecture on Youtube in the materials. I will keep uploading the slides before class and the video after class every week. Hi all, Just a few announcements for the materials and logistics: - I have uploaded the slides and the videos of the first lecture on Youtube in the materials. I will keep uploading the slides before class and the video after class every week. - In materials, you can find also the old exams (similar to what we will do), and exercise sheet 0, which the tutors have prepared. This is a self-assessment, to see if you might want to read up on some of the topics to be able to follow the class. - Here is a Youtube playlist with the classes of the authors of the textbook, which we will be following, so if something is not clear, you have also this additional resource. - I made an error in class and in the Lecture 0 video (fixed in the updated slides): the midterm is actually on Dec 3 from 16:00 to 19:00 (so essentially the time of the class + 1hour, in order to avoid conflicts with other classes), - For the ones who were not in class today, we have democratically decided that the class will be recorded, and it will start at 4pm (not 4:15), with a short 5 minute break around 4:45pm, so the hope is to finish at 5:35ish. See you next week! |
Elements of Machine Learning
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Summary
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In this course we will discuss the foundations – the elements – of machine learning. In particular, we will focus on the ability of, given a data set, to choose an appropriate method for analyzing it, to select the appropriate parameters for the model generated by that method, and to assess the quality of the resulting model. Both theoretical and practical aspects will be covered. Lectures will start on October 15th! Registration in CMS is open until October 31st, in LSF until February 7th. |
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Prerequisites
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The course is targeted at students in computer science, data science and AI, cybersecurity, bioinformatics, math, and general sciences with a mathematical background. Students should know the basics of programming, proof techniques, linear algebra, and statistics, for example by having taken Programming I and II (for programming), Mathematics for Computer Scientists I and II (for linear algebra), and then either Statistics Lab or Mathematics for Computer Scientists III (for statistics). |
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Type |
Basic Lecture (6 ECTS) for BSc DSAI, CySec, and Computer Science; Advanced Lecture (6 ECTS) for all others except for the M.Sc. Cybersecurity. |
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Thursdays, 16:00-18:00 in person in E.2.2 Lecture Hall 0.01 (Günter Hotz Hörsaal). |
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Textbooks
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- Main textbook: Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, and Jonathan Taylor: An Introduction to Statistical Learning with Applications in Python (first edition, 2023) - free pdf (and online classes) - More advanced: Trevor Hastie, Robert Tibshirani and Jerome Friedman: The Elements of Statistical Learning (second edition, 2009) - free pdf |
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Tutorials
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All tutorials will be in person in E1 3 HS001. They will start on Oct 28.
Note that the tutorial on Sunday, 16:00 is a dummy tutorial, intended for students that do not wish to attend any tutorials. |
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Midterms
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In order to access the exam, you will need to pass a midterm with multiple choice questions. This will not count for your final grade, but we will only consider it a pass/fail. Midterm: Thursday, December 3rd, at 16:00-19:00 In case you fail the midterm, we will have also a re-exam for the midterm, so you can still qualify for the exam. Re-exam for the midterm: Saturday, January 16th at 9:00-12:00 Main Exam - Wednesday, February 17th, at 14:00-17:00 |
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Office Hours
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Prof. Dr. Sara Magliacane: before/after each lecture |
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Language |
English |
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Tentative Schedule
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