Introduction to Machine Learning
Weekly outline
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Welcome to the machine learning class!
Machine learning and data analysis are becoming increasingly central in many sciences and applications. In this course, fundamental principles and methods of machine learning will be introduced, analyzed, compared, and implemented.Instructor
Prof. Pascal Fua
Computer Vision Laboratory (CVLAB)
BC 310
E-mail: pascal.fua@epfl.chCourse Times and Locations
Lectures: Monday 16:15 - 18:00 in SG1
Exercises: Tuesday 17:15 - 19:00 in INF1, INJ218, INM10, INM200, INM202
Office Hours
If you have any questions please post them in the discussion forum and one of the TAs will answer you.
Final Exam
The final exam will be closed book and will represent 90% of your final grade. You will be allowed ONE hand-written A4 page of notes.
Exam date: to be determined
Exam room: to be determined
Graded Exercises
Not all practical sessions will be graded. However, two of them will be, one in the middle of the term and another towards the end. This will count for the remaining 10% of your grade. The dates of the graded exercise sessions are shown in the schedule below.
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18.02.2019 Course 19.02.2019 Exercise Session 25.02.2019 Course 26.02.2019 Exercise Session 04.03.2019 Course 05.03.2019 Exercise Session 11.03.2019 Course 12.03.2019 Exercise Session 18.03.2019 Course 19.03.2019 Exercise Session 25.03.2019 Course 26.03.2019 Exercise Session 01.04.2019 Course 02.04.2019 Graded Exercise Session 08.04.2019 Course 09.04.2019 Exercise Session 15.04.2019 Course 16.04.2019 Exercise Session 22.04.2019 No Class (Holiday) 23.04.2019 No Class (Holiday) 29.04.2019 Course 30.04.2019 Exercise Session 06.05.2019 Course 07.05.2019 Exercise Session 13.05.2019 Course 14.05.2019 Graded Exercise Session 20.05.2019 Course 21.05.2019 Exercise Session 27.05.2019 Course 28.05.2019 Exercise Session -
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The results of the first graded session which took place on 2.4.2019. The total number of points is 15.
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The solutions of the first graded exercise
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Introduction to the class and prerequisites
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One of the simplest machine learning techniques.
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Perceptrons and logistic regression.
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From basic perceptrons to Support Vector Machines and AdaBoost.
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From simple perceptrons to sophisticated convolutional networks.
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Linear and non-linear regression.
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Taking the temporal dimension into account
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The Human Brain as a Source of Inspiration
