This repository is devoted to Machine Learning reading group held in Faculty of Mathematics and Computer Science, Transilvania University of Brasov.
Weekely meetings are scheduled in Faculty of Mathematics and Computer Science, Iuliu Maniu 50, officially starting with October 31st 2018.
Ph.D. students, researchers and interested people are expected to actively participate to these meetings.
- Presenting and discussing research papers
- Discussing selected books chapters
- Workshops
Books:
- Duda et al. book, with errata.
Every TuesdayThursday, 18-19.30 Bucharest time, room PII2online.
11 February 2021, 18.00, [Workshop] Data visualization by dimensionality reduction, Alexandru Ionescu, Ph.D., Faculty of Mathematics and Computer Science, Transilvania University of Brasov. Prerequisites: Jupyter notebook; Starting notebook & Solution; meeting link: https://meet.google.com/pnu-wakf-vyk;
14 January 2021, 18.00, Paper presentation and discussion: "An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling"; Lucian M. Sasu, Ph.D., Faculty of Mathematics and Computer Science, Transilvania University of Brasov; meeting link https://meet.google.com/pnu-wakf-vyk; Presentation
17 December 2020, 18.00, [Workshop] AlphaX: A tasting of Reinforcement Learning through the Tic Tac Toe game, Kerestély Árpád, Ph.D. Student, Faculty of Mathematics and Computer Science, Transilvania University of Brasov. Prerequisites: Visual Studio 2019 with C++ development installed. Workproject & Solution. Meeting link: https://meet.google.com/pnu-wakf-vyk;
3 December 2020, 18.00, Paper presentation and discussion: "AI for Cheat Detection in Online Games"; Delia Duca-Iliescu, Ph.D. Student, teacher assistant, Faculty of Mathematics and Computer Science, Transilvania University of Brasov; meeting link https://meet.google.com/pnu-wakf-vyk;
19 November 2020, 18.00, Paper presentation and discussion: "Attention is all you need", https://arxiv.org/abs/1706.03762; Honorius Galmeanu, Ph.D., Lecturer, Faculty of Mathematics and Computer Science, Transilvania University of Brasov, and ML Engineer Xperi Corp; meeting link https://meet.google.com/pnu-wakf-vyk; Presentation
5 November 2020, 18.00, "Artificial Intelligence for Medical Image Understanding", Florin Ghesu, Ph.D., Staff Research Scientist at Siemens Healthineers; meeting link https://meet.google.com/pnu-wakf-vyk
22 October 2020, 18.00, "Temporal coding and backpropagation in spiking neural networks", Iulia Comsa, Ph.D., Google Research; meeting link https://meet.google.com/pnu-wakf-vyk
Postponed due to University's regulation related to Coronavirus 10 March 2020, [Workshop] Introductio to PyTorch, by. Lucian Sasu, Ph.D. {Presentation to be added}, Prerequisites: Python installed, PyTorch with Jupyter notebook/VS Code/Pycharm environments.
14 January 2020, [Workshop] Dimensionality reduction, by. Alexandru Ionescu, Ph.D. Worksheet, Solution Prerequisites: Python installed, with Jupyter/VS Code/Pycharm environments.
10 December 2019, [Workshop] Backprop refresh, by Ioana Plajer, Ph.D. Prerequisites: Python installed, with Jupyter/VS Code/Pycharm environments. Worksheet, Solution(Part 2/2)
3 December 2019, [Workshop] Backprop refresh, by Ioana Plajer, Ph.D. Prerequisites: Python installed, with Jupyter/VS Code/Pycharm environments. Worksheet, (Part 1/2)
19 November 2019, [Workshop] Advanced gradient descent optimization algorithms (4): Adam, by Mihaela Olteanu, Ph.D. student. Prerequisites: Python installed, with Jupyter/VS Code/Pycharm environments. Adam worksheet
12 November 2019, [Workshop] Advanced gradient descent optimization algorithms (3): AdaDelta, by Bogdan Ivan, student. Prerequisites: Python installed, with Jupyter/VS Code/Pycharm environments. AdaDelta worksheet Solution
5 November 2019, [Workshop] Advanced gradient descent optimization algorithms (2): Adagrad, by Ioana Plajer, Ph.D. Prerequisites: Python installed, with Jupyter/VS Code/Pycharm environments. AdaGrad worksheet AdaGrad solution
29 October 2019, [Workshop] Advanced gradient descent optimization algorithms (1): Momentum, by Lucian Sasu, Ph.D. Prerequisites: Python installed, with Jupyter/VS Code/Pycharm environments. Workshop material, Solution notebook
15 October 2019, [Workshop] Stochastic gradient decent hands-on presented by Lucian M. Sasu, Ph.D. Prerequisites: Python installed, with Jupyter/VS Code/Pycharm environments. Presentation
8 October 2019, [Workshop] Introduction to a Pythonic machine learning environment presented by Lucian M. Sasu, Ph.D. and Kerestély Árpád, Ph.D. student. Prerequisites & Presentation
28 May 2019, Chapter six "Multilayer neural networks" of Pattern Classification, part 2, presented by Luciana Majercsik, Ph.D. student, and Kerestély Árpád, Ph.D. student. slides
14, 21 May 2019, not held.
7 May 2019, Laplacian Eigenmaps for Dimensionality Reduction and Data Representation, Alexandru Ionescu, Ph.D.; slides
30 April 2019, university holiday
23 April 2019, not held.
16 April 2019, Universal approximation for NNs, Lucian M. Sasu, Ph.D.
9 April 2019, Chapter six "Multilayer neural networks" of Pattern Classification, part 1, presented by Luciana Majercsik, Ph.D. student, and Kerestély Árpád, Ph.D. student. slides and pycode
2 April 2019, Efficient Estimation of Word Representations in Vector Space, Mihaela Olteanu, Ph.D. student.
28 March 2019, Support Vector Machines, Honorius Galmeanu, Ph.D.. slides
19 March 2019, Machine learning in protein engineering, Marius Paltanea, Ph.D. student; slides.
12 March 2019, 2nd part from Chapter five "Linear discriminant analysis" of Pattern Classification, presented by Luciana Majercsik, Ph.D. student.
5 March 2019 CANCELLED.
26 February 2019, 1st part from Chapter five "Linear discriminant analysis" of Pattern Classification, presented by Luciana Majercsik, Ph.D. student.
19 February 2019, Inter-semestrial holiday.
12 February 2019, Transfer entropy-based feedback improves performance in artificial neural networks, Adrian Moldovan, Ph.D. student.
5 February 2019, 2nd part from Chapter four "Nonparametric Techniques" of Pattern Classification, presented by Delia Duca-Iliescu, Ph.D. student; slides
29 January 2019, DensePose: Dense Human Pose Estimation In The Wild, Vlad Ionescu, Ph.D. student; slides
22 January 2019, 1st part from Chapter four "Nonparametric Techniques" of Pattern Classification, presented by Alexandru Ionescu, Ph.D.; slides
16 January 2019, Gaussian Material Synthesis, Vlad Vrabie, Applied Informatics student; slides
12 December 2018, 2nd part from Chapter three "Maximum-Likelihood and Bayesian Parameter Estimation" - of Pattern Classification, Luciana Majercsik, Ph.D. student; slides
5 December 2018, Issues in Using Self-Organizing Maps in Human Movement and Sport Science, Vlad Ionescu, Ph.D. student; slides
28 November 2018, 1st part from Chapter three of "Maximum-Likelihood and Bayesian Parameter Estimation" - of Pattern Classification, Luciana Majercsik, Ph.D. student; slides
21 November 2018, A Comparative Study of Game Tree Searching Methods, Delia Duca-Iliescu, Ph.D. student
14 November 2018, Chapter two "Bayesian Decision Theory" - of Pattern Classification, presented by Alexandru Ionescu, Ph.D.; slides
7 November 2018, Speeding Up Distributed Machine Learning Using Codes presented by Kerestély Árpád, Ph.D. student
24 October 2018, Semi-Supervised Learning on Riemannian Manifolds presented by Alexandru Ionescu, Ph.D..
13 oct 2018, First chapter of Pattern Classification, presented by Kerestély Árpád, Ph.D. student