BEV033DLE – Deep Learning

The course introduces deep neural networks and deep learning – 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. The 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.

• Teachers: Boris Flach, Alexander Shekhovtsov and Jan Šochman
• 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
• Schedule: Summer semester 2024
  • Lectures: Wed, 11:00-12:30, KN:E-301
  • Labs/Seminars: Thu, 9:15-10:45 in KN:E-126 and 11:00-12:30 in KN:E-126.

• 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: 50% practical labs + 50% written exam = 100% (+ bonus points)
  • Credits: 6 CP

• Textbooks and References:
  • I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, MIT Press, 2016

• Lectures
• Labs and Seminars
• Course Overview and Grading
• Solved Examples