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magistraleinformatica:aa2:start [29/04/2015 alle 14:47 (9 anni fa)]
Davide Bacciu [Lectures] |
magistraleinformatica:aa2:start [04/04/2016 alle 10:20 (8 anni fa)] (versione attuale)
Davide Bacciu Communication on course replacement for year 2015/2016 |
| ===== News ===== | ===== News ===== |
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| **(02/04/2015)** List of midterm assignments to students is now out | **(04/04/2016) Note for Students of Academic Year 2015/2016** The AA2 course is **inactive** during year 2015/2016. Students interested in the course can take the replacement course [[http://didawiki.di.unipi.it/doku.php/bionics-engineering/computational-neuroscience/start|"Computational Neuroscience"]] from the M.Sc. in Bionics Engineering. |
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| | (02/04/2015) List of midterm assignments to students is now out |
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| (13/03/2015) Midterm reading list and dates now out | (13/03/2015) Midterm reading list and dates now out |
| The course hosts guest seminars by national and international researchers working on the field as well as by companies that are engaged in the development of advanced applications using machine learning models. | The course hosts guest seminars by national and international researchers working on the field as well as by companies that are engaged in the development of advanced applications using machine learning models. |
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| //Topics covered// - dynamical recurrent neural networks; reservoir computing; graphical models and Bayesian learning; hidden Markov models; conditional random fields; latent variable models; non-parametric and kernel-based methods; learning in structured domains (sequences, trees and graphs); unsupervised learning for complex data; deep learning; emerging topics and applications in machine learning. | //Topics covered// - dynamical recurrent neural networks; reservoir computing; graphical models and Bayesian learning; hidden Markov models; Markov random fields; latent variable models; non-parametric and kernel-based methods; learning in structured domains (sequences, trees and graphs); unsupervised learning for complex data; deep learning; emerging topics and applications in machine learning. |
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| **Textbook and Teaching Materials** | **Textbook and Teaching Materials** |
| | 15 | 23/4/15 (14-16) | C1 | Reservoir Computing for Trees and Graphs (guest lecture by Claudio Gallicchio) {{:magistraleinformatica:aa2:rnn5-rc4struct.pdf| slides}} | [[magistraleinformatica:aa2:start#further_readings|[14]]] TreeEsn \\ [[magistraleinformatica:aa2:start#further_readings|[15]]] GraphEsn | [[magistraleinformatica:aa2:start#further_readings|[16]]] Additional on GraphEsn \\ [[magistraleinformatica:aa2:start#further_readings|[17]]] Constructive NN for graphs | | | 15 | 23/4/15 (14-16) | C1 | Reservoir Computing for Trees and Graphs (guest lecture by Claudio Gallicchio) {{:magistraleinformatica:aa2:rnn5-rc4struct.pdf| slides}} | [[magistraleinformatica:aa2:start#further_readings|[14]]] TreeEsn \\ [[magistraleinformatica:aa2:start#further_readings|[15]]] GraphEsn | [[magistraleinformatica:aa2:start#further_readings|[16]]] Additional on GraphEsn \\ [[magistraleinformatica:aa2:start#further_readings|[17]]] Constructive NN for graphs | |
| | 16 | 27/4/15 (11-13) | C1 | Deep Learning {{:magistraleinformatica:aa2:generative-7-hand.pdf| slides}}| [[magistraleinformatica:aa2:start#further_readings|[18]]] A classic divulgative paper from the initiator of Deep Learning \\ [[magistraleinformatica:aa2:start#further_readings|[19]]] Recent review paper \\ | [[magistraleinformatica:aa2:start#further_readings|[20]]] A freely available book on deep learning from Microsoft RC | | | 16 | 27/4/15 (11-13) | C1 | Deep Learning {{:magistraleinformatica:aa2:generative-7-hand.pdf| slides}}| [[magistraleinformatica:aa2:start#further_readings|[18]]] A classic divulgative paper from the initiator of Deep Learning \\ [[magistraleinformatica:aa2:start#further_readings|[19]]] Recent review paper \\ | [[magistraleinformatica:aa2:start#further_readings|[20]]] A freely available book on deep learning from Microsoft RC | |
| | 17 | 30/4/15 (14-16) | C1 | Kernel and non-parametric methods: kernel method refresher; kernels for complex data (sequences, trees and graphs); convolutional kernels; adaptive kernels | [KM] Chapters 2 and 9 - Kernel methods refresher and kernel construction \\ [KM] Chapter 11 - Kernels for structured data \\ [KM] Chapter 12 - Generative kernels | | | | 17 | 30/4/15 (14-16) | C1 | Kernel and non-parametric methods: kernel method refresher; kernels for complex data (sequences, trees and graphs); convolutional kernels; adaptive kernels {{:magistraleinformatica:aa2:kernel-1-hand.pdf| slides}} | [KM] Chapters 2 and 9 - Kernel methods refresher and kernel construction \\ [KM] Chapter 11 - Kernels for structured data \\ [KM] Chapter 12 - Generative kernels | [[magistraleinformatica:aa2:start#further_readings|[21]]] Generative kernels on hidden states multisets | |
| | 18 | 04/5/15 (11-13) | C1 | Kernel and non-parametric methods: Linear and Non-Linear Dimensionality Reduction (guest lecture by [[http://ekvv.uni-bielefeld.de/pers_publ/publ/PersonDetail.jsp?personId=34943216&lang=en|Alexander Schulz]]) | | | | | 18 | 04/5/15 (11-13) | C1 | Kernel and non-parametric methods: Linear and Non-Linear Dimensionality Reduction (guest lecture by [[http://ekvv.uni-bielefeld.de/pers_publ/publ/PersonDetail.jsp?personId=34943216&lang=en|Alexander Schulz]]) {{:magistraleinformatica:aa2:linearandnonlineardr.pdf| slides}} | [BRML] Sect. 15.1-15.2 PCA \\ [BRML] Sect. 15.7 Kernel PCA | [[magistraleinformatica:aa2:start#further_readings|[22]]] t-SNE | |
| | 19 | 07/5/15 (14-16) | C1 | Kernel and non-parametric methods: Recent Advances in Dimensionality Reduction (guest lecture by [[http://ekvv.uni-bielefeld.de/pers_publ/publ/PersonDetail.jsp?personId=34943216&lang=en|Alexander Schulz]]) | | | | | 19 | 07/5/15 (14-16) | C1 | Kernel and non-parametric methods: Recent Advances in Dimensionality Reduction (guest lecture by [[http://ekvv.uni-bielefeld.de/pers_publ/publ/PersonDetail.jsp?personId=34943216&lang=en|Alexander Schulz]]) {{:magistraleinformatica:aa2:linearandnonlineardr.pdf| slides}} | | | |
| | | 20 | 11/5/15 (11-13) | C1 | An Overview of ML research at UNIPI; final project proposals | | | |
| | | 21 | 18/5/15 (11-13) | C1 | Company Talk: [[http://www.henesis.eu/|Henesis]] (Artificial Perception) | | | |
| | | 22 | 21/5/15 (14-16) | C1 | Company Talk: [[http://kode-solutions.net/|Kode]] Solutions | | | |
| | | 23 | 21/5/15 (16-18) | C1 | Final lecture: course wrap-up; final project assignments; exam information | | | |
| ===== Exams ===== | ===== Exams ===== |
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| Students must select the project type and topic before the last lecture of the course. The project report/software should be handled (at least) 7 days before its [[magistraleinformatica:aa2:start#oral_presentation|oral presentation]]. | Students must select the project type and topic before the last lecture of the course. The project report/software should be handled (at least) 7 days before its [[magistraleinformatica:aa2:start#oral_presentation|oral presentation]]. |
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| | **NEW!!** Project reports should be formatted using the provided {{:magistraleinformatica:aa2:final-report-tex.zip|LaTex}} or {{:magistraleinformatica:aa2:final-report.doc|MS Word}} templates. |
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| ==== Oral Presentation ==== | ==== Oral Presentation ==== |
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| [[http://research.microsoft.com/apps/pubs/default.aspx?id=209355|[20]]] L. Deng and D. Yu. Deep Learning Methods and Applications, 2014 | [[http://research.microsoft.com/apps/pubs/default.aspx?id=209355|[20]]] L. Deng and D. Yu. Deep Learning Methods and Applications, 2014 |
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| | [[http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6889768&tag=1|[21]]] D. Bacciu, A. Micheli and A. Sperduti, Integrating Bi-directional Contexts in a Generative Kernel for Trees, Proceedings of the 2014 IEEE International Joint Conference on Neural Networks (IJCNN'14), pp.4145 - 4151, IEEE, 2014 |
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| | [[http://www.jmlr.org/papers/volume9/vandermaaten08a/vandermaaten08a.pdf|[22]]] L. van der Maaten, G. Hinton, Visualizing Data using t-SNE, Journal of Machine Learning Research, Vol. 9, pp. 2579-2605, 2008 |
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