Convolutional Neural Networks

• 26 hrs lectures
• 26 hrs projects

• 35 pts mid-term test (18 pts written part, 17 pts test part)
• 65 pts projects

Students will gain basic knowledge of convolutional neural networks, their training (optimization), their building blocks and of the tools and software frameworks used to implement them. Students will gain insights in what factors determine accuracy of networks in real applications including data sets, loss functions, network structure, regularization, optimization, overfitting and multi-task learning. They will receive an overview of state-of-the-art networks in a range of computer vision tasks (classification, object detection, segmentation, identification), speech recognition, language understanding, data generation and reinforcement learning.
Students will acquire team work experience during project work and they will acquire basic knowledge of python libraries for linear algebra and machine learning.

Basic knowledge of convolutional neural networks, their capabilities and limitations. Practical applications mostly in computer vision tasks and completed by task from speech recognition and language processing. To allow students to design overall solutions using convolutional networks in practical applications including network architectures, optimization, data collection and testing and evaluation.

This course is for you whether your goal is to work as an AI expert in in a large multinational corporation such as Google or Facebook, whether you want to push forward frontiers of artificial intelligence as a part of a top academic team, or whether you just want to broaden your horizons of the state-of-the-art in machine learning. Neural networks are at the core of many commercial applications ranging from speech recognition, content-based image search and intelligent surveillance systems to question answering systems and autonomous cars. At the same time neural networks are the enabling factor of the current rapid advancements in artificial intelligence. This course will enable you to use this powerful tool in practical applications.

Basics of linear algebra (multiplication of vectors and matrices), differential calculus (partial derivatives, chain rule), Python and intuitive understanding of probability (e.g. conditional probability). Any knowledge of machine learning and image processing is an advantage.

• Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016.
• Li, Fei-Fei, et al.: CS231n: Convolutional Neural Networks for Visual Recognition. Stanford, 2018.
• Bishop, C. M.: Pattern Recognition, Springer Science + Business Media, LLC, 2006, ISBN 0-387-31073-8.

1. Introduction, linear models. loss function, learning algorithms and evaluation. (Organization, basics of neural networks, numpy tutorial)
2. Fully connected networks, loss functions for classification and regression. (PyTorch simple regression,  Image Classification) 
3. Convolutional networks, locality in equivariance of computation. (Lecture)
4. Generalization, regularization, data augmentation. multi-task learning and pre-training. (Optimizers)
5. Problems during network optimization and batch normalization. Existing image classification network architectures. (Image classification networks)
6. Object detection: MTCNN face detector, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD.
7. Semantic and instance segmentation. Connections to estimation of depth, surface normals, shading and motion.
8. Learning similarity and embedding. Person identification. 
9. Recurrent networks and sequence processing (text and speech). Connectionist Temporal Classification (CTC). Attention networks.
10. Language models. Basic image captioning networks, question answering and language translation.
11. Generative models. Autoregressive factorization. Generative Adversarial Networks (GAN, DCGAN, cycle GAN).
12. Reinforcement learning. Deep Q-network (DQN) and policy gradients.
13. Overview of emerging applications and cutting edge research.

Team project (2-3 students).
Individual assignments - proposed by students, approved by the teacher. Components:

• Problem Formulation, team formation.
• Research of existing solutions and usefull tools.
• Baseline solution and evaluation proposal.
• Data collection.
• Experiments, testing and gradual improvement.
• Final report and Presentation of the project.

• Project concluded by public presentation - 65 points.
• Three tests during the semester - 35 points.

Exam prerequisites:
Acquiring at least 50 points.

Acquiring at least 50 points.