BEV033DLE โ€“ Deep Learning

Overview

The course introduces deep neural networks and deep learning โ€“ a branch of machine learning and artificial intelligence. It aims at providing the relevant algorithmic and theoretical concepts needed for successfully designing and training NNs. At the same time it strives at providing technical and practical skills in this domain.

This course is best suited for bachelor students in their last year and for master students. We expect students to have basic knowledge in machine learning and AI.

News

  • 13.04.2021 Dear Students, after the discussion about problems and difficulties you encounter in this course, we made several proposals which will hopefully help you to achieve the course goals. Please see the discussion forum for more details.
  • 10.01.2021 Dear Students, please enrol also for one of the two parallel labs (in KOS). Otherwise you will not appear in the upload system BRUTE and will not receive invitations to the BBB meeting rooms for lectures/labs.
  • 09.01.2021 Dear Students, unfortunately for all of us, teaching in this semester will start in online mode. We will use the BigBlueButton platform (BBB), which runs safely and securely on local university servers. All you need for joining the virtual teaching rooms is a web-browser and a headset. If you are not familiar with this platform, please have a look at https://bigbluebutton.org/ and the demo videos provided there. We will schedule the meetings to start a few minutes ahead of the regular time, so that you can set up your gear and join them without hurry. You will receive invitation e-mails well before each scheduled meeting. The BBB platform is integrated in the faculty upload system BRUTE. Therefore you can also join the meetings via BRUTE.

Details

  • Prerequisites:
    • Fundamentals of mathematics comparable to the following courses: Linear Algebra (B0B01LAG), Calculus (B0B01MA2), Optimization (B0B33OPT) as well as Probability, Statistics, and Theory of Information (B0B01PST ).
    • Besides proficient knowledge of mathematics as given above, students are expected to have solid knowledge in the following areas of computer science and artificial intelligence: basics of graph theory and related algorithms; basics of pattern recognition, empirical risk minimisation, linear classifiers, support vector machines as in Pattern Recognition and Machine Learning (B4B33RPZ or BE4B33RPZ).
  • Course format: (2/2)
    • lectures: weekly
    • alternating practical and theoretical labs (tutorials): weekly
    • Lectures: Wed, 11:00-12:30, online (KN:E-126)
    • Labs/Seminars: Thu, 9:15-10:45 and 11:00-12:30, both online (KN:E-128)
  • Grading/Credits:
    • Thresholds for passing: at least 50% of the regular points in the labs and at least 50% of the regular points in the exam
    • Weights for final grading: 40% practical labs + 60% written exam = 100% (+ bonus points)
    • Credits: 6 CP
  • Textbooks and References:
    • I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016

Materials

courses/bev033dle/start.txt ยท Last modified: 2021/04/13 09:39 by flachbor