Course detail

Convolutional Neural Networks

FIT-KNNAcad. year: 2024/2025

Solutions based on machine learning approaches gradually replace more and more hand-designed solutions in many areas of software development, especially in perceptual task focused on information extraction from unstructured sources like cameras and microphones. Today, the dominant method in machine learning is neural networks and their convolutional variants. These approaches are at the core of many commercially successful applications and they push forward the frontiers of artificial intelligence.

Language of instruction

Czech

Number of ECTS credits

Mode of study

Not applicable.

Guarantor

Ing. Michal Hradiš, Ph.D.

Department

Department of Computer Graphics and Multimedia (UPGM)

Entry knowledge

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.

Rules for evaluation and completion of the course

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

Aims

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.
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.

Study aids

Not applicable.

Prerequisites and corequisites

Not applicable.

Basic literature

Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, 2016. (EN)

Type of course unit

Lecture

26 hod., optionally

Teacher / Lecturer

Ing. Michal Hradiš, Ph.D.

Syllabus

Introduction, linear models. loss function, learning algorithms and evaluation. (organization, NN review)
Fully connected networks, loss functions for classification and regression. (prezentation)
Convolutional networks, locality in equivariance of computation, weight initialization, batch normalization. (prezentation, weight init. tutorial)
Network architectures for image classification. (prezentation)
Generalization, regularization, data augmentation. multi-task learning, semi supervised learning, active learning, self-supervised learning. (prezentation)
Object detection: MTCNN face detector, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD. (prezentation including image segmentation)
Semantic and instance segmentation. Connections to estimation of depth, surface normals, shading and motion.
Learning similarity and embedding. Person identification. (prezentation)
Recurrent networks and sequence processing (text and speech). Connectionist Temporal Classification (CTC). Attention networks. (prezentation)
Language models. Basic image captioning networks, question answering and language translation. (prezentation)
Generative models. Autoregressive factorization. Generative Adversarial Networks (GAN, DCGAN, cycle GAN). (prezentation)
Reinforcement learning. Deep Q-network (DQN) and policy gradients. (prezentation)
Overview of emerging applications and cutting edge research.

Project

26 hod., compulsory

Teacher / Lecturer

Ing. Michal Hradiš, Ph.D.

Syllabus

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.

VUT

Faculties

University Institutes

Parts

Convolutional Neural Networks

Type of course unit