Lectures

You can download the lectures here. We will try to upload lectures prior to their corresponding classes.

• Introduction
  tl;dr: Introduction to the course objectives and main outcomes. Machine learning introduction.
  [S00] [S01] [S02]
  
• Data Representation with Tensors
  tl;dr: Data Representation with Tensors.
  [S03]
  
• Learning from Tensors, Deep Learning for Classification
  tl;dr: Learning from Tensors: Gradient Descent and Backpropagation. Designing and Improving Deep Learning Models.
  [S04] [S05]
  

  Suggested Readings:

  • LeCun, Yann, et al. “Gradient-based learning applied to document recognition.” Proceedings of the IEEE 86.11 (1998): 2278-2324.
  • Srivastava, Nitish, et al. “Dropout: a simple way to prevent neural networks from overfitting.” The journal of machine learning research 15.1 (2014): 1929-1958.
  • Ioffe, Sergey, and Christian Szegedy. “Batch normalization: Accelerating deep network training by reducing internal covariate shift.” International conference on machine learning. pmlr, 2015.
  • He, Kaiming, et al. “Deep residual learning for image recognition.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
• Object Detection
  tl;dr: Object Detection.
  [S06]
  

  Suggested Readings:

  • Redmon, Joseph, et al. “You only look once: Unified, real-time object detection.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
• Handling scientific Experiments
  tl;dr: Handling scientific experiments.
  [S07]