Invitation to join 2021 Spring School 'CVML Short Course - Machine Learning and Deep Neural Networks', 27-28th April 2021
Dear Machine Learning and Deep Neural Networks engineers, scientists and enthusiasts,
You are welcomed to register in this CVML Short e-course on ‘Machine Learning and Deep Neural Networks’, 27-28thApril 2021: https://icarus.csd.auth.gr/spring-cvml-short-course-machine-learning-and-deep-neural-networks/
It will take place as a two-day e-course (due to COVID-19 circumstances), hosted by the Aristotle University of Thessaloniki (AUTH), Thessaloniki, Greece, providing a series of live lectures delivered through a tele-education platform. They will be complemented with on-line video recorded lectures and lecture pdfs, to facilitate international participants having time difference issues and to enable you to study at own pace. You can also self-assess your knowledge, by filling appropriate questionnaires (one per lecture). You will be provided programming exercises to improve your programming skills.
It is part of the very successful CVML short course series that took place in the last three years.
Course description ‘Machine Learning and Deep Neural Networks’
The short e-course consists of 16 1-hour live lectures organized in two Parts (1 Part per day):
Part A lectures (8 hours) provide an in-depth presentation of Deep Neural Networks, which are at the forefront of AI advances today, starting with introduction to Machine Learning. Then the cornerstone DNN theory and technologies are presented: a) Artificial Neural Networks, Perceptron; b) Multilayer perceptron, Backpropagation; c) Deep neural networks. Both data classification and regression problems are treated. Convolutional NNs; d) Recurrent Neural Networks; e) Attention and Transformers. Applications follow in several image analysis, computer vision and autonomous system applications, notably: a) Deep learning for object detection and b) Deep Semantic Image Segmentation. Finally, Generative Adversarial Networks are presented that promise to revolutionize the way we create media/arts, while seriously threatening our democracy with fake data creation and spread.
Part B lectures (8 hours) provide fan in-depth presentation of Machine Learning to complement DNNs. Unsupervised Learning (Data Clustering) is first detailed, allowing us to find structure and extract concepts/knowledge from huge high-dimensionality data. Then Supervised Learning (Data Classification) techniques are presented, notably: a) Decision surfaces (whose special case is DNNs and SVMs) and b) Distance based classification. Dimensionality reduction techniques are overviewed, allowing us to visualize high-dimensionality data found in most applications, ranging from Medicine to Financial Engineering. Kernel methods are presented that can boost performance of any linear ML operation (e.g., PCA, K-means etc). Bayesian learning provides a unified theoretical framework that can encompass many of the ML approaches. Deep Reinforcement Learning is also presented, as it is an essential element in novel Robotics/Control and other decision-making application domains. Finally, CVML programming tools (e.g., DNN frameworks, BLAS/cuBLAS, DNN and CV libraries) are overviewed, as they allow fast application of all the above knowledge in almost any application domain.
Part A: Deep Neural networks (first day, 8 lectures)
- Introduction to Machine Learning
- Artificial Neural Networks, Perceptron
- Multilayer perceptron. Backpropagation
- Deep neural networks. Convolutional NNs
- Recurrent Neural Networks. LSTMs
- Attention and Transformers
- Deep learning for object detection
- Deep Semantic Image Segmentation
Part B: Machine Learning. Pattern Recognition (second day, 8 lectures)
- Generative Adversarial Networks
- Data Clustering
- Decision Surfaces. Support Vector Machines
- Dimensionality Reduction
- Kernel Methods
- Bayesian Learning
- Deep Reinforcement Learning
- CVML Software Development Tools
Though independent, the attendees of this short e-course will greatly benefit by attending the CVML Short e-course on ‘Computer Vision for Autonomous Systems’ 5-6th May 2021:
You can use the following link for course registration:
Lecture topics, sample lecture ppts and videos, self-assessment questionnaires and programming exercises can be found therein.
For questions, please contact: Ioanna Koroni <email@example.com>
The short course is organized by Prof. I. Pitas, IEEE and EURASIP fellow, Chair of the IEEE SPS Autonomous Systems Initiative, Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab), Aristotle University of Thessaloniki, Greece, Coordinator of the European Horizon2020 R&D project Multidrone. He is ranked 249-top Computer Science and Electronics scientist internationally by Guide2research (2018). He is head of the EC funded AI doctoral school of Horizon2020 EU funded R&D project AI4Media (1 of the 4 in Europe). He has 32200+ citations to his work and h-index 85+.
AUTH is ranked 153/182 internationally in Computer Science/Engineering, respectively, in USNews ranking.
1) Prof. I. Pitas:
2) Horizon2020 EU funded R&D project Aerial-Core: https://aerial-core.eu/
3) Horizon2020 EU funded R&D project Multidrone: https://multidrone.eu/
4) Horizon2020 EU funded R&D project AI4Media: https://ai4media.eu/
5) AIIA Lab: https://aiia.csd.auth.gr/
Prof. I. Pitas
Director of the Artificial Intelligence and Information analysis Lab (AIIA Lab)
Aristotle University of Thessaloniki, Greece