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TIES4910 Deep-Learning for Cognitive Computing, Theory, 5 ECTS [home page]

Information technology (MIT)

Announcement:
The language used was temporarily changed. Personal information page allows you to save your language settings.
You cannot register for the course because the course has expired.
The registration deadline for this course passed 30.9.17 at 23:59.

General information

Home page: http://www.cs.jyu.fi/ai/vagan/DL4CC.html
Begins - ends: 4.9.17 - 20.10.17
Registration period: 1.8.17 at 0:00 - 30.9.17 at 23:59
The registration may be cancelled before 20.10.2017 at 23:59.
Instructor(s): Vagan Terziyan (vagan.terziyan@jyu.fi)
Credits: 5 ECTS cr.
Languages: language(s) of instruction: English; completion language(s): English
Registered: 68
Max participants: 100
Still room for: 32
Organisations:Faculty of Information Technology (IT), Information technology (MIT) (TIE)
Contents:The course Deep Learning for Cognitive Computing (10 credits, delivered in English) is an evolution of the course ITKA-352: “Introduction to Watson Technologies”, which aims to provide more systematic, structured (broader, deeper and multidisciplinary) view to this popular domain. The major objectives of the course are as follows:

· To describe challenges and opportunities within the emerging Cognitive Computing domain and professions around it;

· To summarize role and relationships of Cognitive Computing within the network of closely related scientific domains, professional fields and courses of the faculty (e.g., Artificial Intelligence, Semantic and Agent Technologies, Big Data Analytics, Semantic Web and Linked Data; Cloud Computing, Internet of Things, etc.);

· To introduce the major providers of cognitive computing services (Intelligence-as-a-Service) on the market (e.g., IBM Watson, Google DeepMind, Microsoft Cognitive Services, etc.) and show demos of their services (e.g., text, speech, image, sentiment, etc., processing, analysis, recognition, diagnostics, prediction, etc.);

· To give introduction on major theories, methods and algorithms used within cognitive computing services with particular focus on Deep Learning technology;

· To provide “friendly” (with reasonable amount of mathematics) introduction to Deep Learning (including variations of deep Neural Networks and approaches to train them);

· To provide different views to this knowledge suitable to people with different backgrounds and study objectives (ordinary user, advanced user, software engineer, domain professional, data scientist, cognitive analyst, mathematician, etc.);

· To discuss scientific challenges and open issues within the domain as well as to share with the students information on relevant ongoing projects in the Faculty;

· To train within teams to use available cognitive services via GUIs or APIs for inventing new interesting use cases and designing own applications;

· For advanced students there will be a possibility to contribute (enhance, optimize, etc.) known algorithms or the related science behind them.

We believe that knowledge on Cognitive Computing at least at the level of an advanced user of it would be an excellent added value within the portfolio of every professional (from very humanitarian to very technical one).

We will combine overview lectures, self-study, group-work, theoretical and practical assignments trying to find an optimal approach to everyone.
Learning outcomes:Knowledge on Cognitive Computing domain and available related services;
Understanding of the Deep Learning "philosophy";
Knowledge on Neural Nets and Deep Neural Nets (Recurrent, Convolutional, etc.) and their learning approaches;
Capability to utilize Cognitive Computing services (from IBM , Google, Microsoft, etc.) as an advanced user
Prerequisites:Look for details here (http://www.cs.jyu.fi/ai/vagan/DL4CC.html)
Completion mode:Assignment
Literature:
Materials

Lectures consist of 4 parts (groups) as follows:

· Part I. (Lectures 1-3):

o Topic: Cognitive Computing: Intelligence-as-a-Service (IBM Watson, Google’s Deep Mind & Microsoft Cognitive Services)

o See the lecture slides (http://www.cs.jyu.fi/ai/vagan/DL4CC_Part-1.pptx ; download before viewing and switch on speakers).

o Our concerns within the Part-I:

- Why and what is Cognitive Computing;

- Why to study;

- Cognitive Computing as a context for other technologies (e.g., Semantic and Agent Technologies, IoT, Industry 4.0, etc.);

- What is available on the market of “Intelligence-as-a-Service” for users and developers (from IBM Watson, DeepMind / Google, Cognitive Services of Microsoft, etc.);

- How all these related to “Deep Learning”.


· Part II. (Lectures 4-6):

o Topic: Introduction to Neural Networks and Deep Learning

o See the lecture slides (http://www.cs.jyu.fi/ai/vagan/DL4CC_Part-2.pptx ; download before viewing).

o Our concerns within the Part-II:

- Deep Learning for beginners;

- Neural nets basics;

- Gradient descent and backpropagation;

- Variations of deep learning approaches and architectures.


· Part III. (Lectures 7-8):

o Topic: Convolutional Neural Networks for Image Processing

o See the lecture slides ( http://www.cs.jyu.fi/ai/vagan/DL4CC_Part-3.pptx; download before viewing).

o Our concerns within the Part-III:

- What is Convolutional Neural Network and how it works;

- What kind of architecture has a Convolutional Neural Network;

- How a Convolutional Neural Network processes images;

- How to train a Convolutional Neural Network.


· Part IV. (Lectures 9-10):

o Topic: Neural Networks with Memory: Recurrent Neural Networks and LSTM Networks

o See the lecture slides ( http://www.cs.jyu.fi/ai/vagan/DL4CC_Part-4.pptx; download before viewing).

o Our concerns within the Part-IV:

- What is Recurrent Neural Network (RNN) and how it works;

- What is Long Short-Term Memory (LSTM) network and how it works;

- How they are used;

- How to train a RNN and LSTM.

Literature

ISBNBook information
Michael Nielsen (2017). Neural Networks and Deep Learning. (http://neuralnetworksanddeeplearning.com/)
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning, MIT Press, 787 pp. (http://www.deeplearningbook.org)
Deep Learning Resources (http://deeplearning.net/)

Further information:Look for details here (http://www.cs.jyu.fi/ai/vagan/DL4CC.html)
Evaluation:
Grade scale

0-5

Assessment criteria

Look for details here (http://www.cs.jyu.fi/ai/vagan/DL4CC.html)

Assessment criteria for each grade

Look for details here (http://www.cs.jyu.fi/ai/vagan/DL4CC.html)

[Limit information on study groups]

Luento [group details and registration]

Luento 1 [group details and registration]; registered 53, maximum 100
reg.time: 1.8.2017 00:00 - 30.9.2017 23:59
 LocationWeekDayDateAtSupervisorFurther informationURITapahtuman tiedot
1Ag D215.136Mo4.9.201710:15-12:00TerziyanTapahtuman tiedot
2Ag D221.338Mo18.9.201710:15-12:00TerziyanTapahtuman tiedot
3Ag C23239Mo25.9.201710:15-12:00TerziyanTapahtuman tiedot
4Ag C23240Mo2.10.201710:15-12:00TerziyanTapahtuman tiedot
5Ag C23241Mo9.10.201710:15-12:00TerziyanTapahtuman tiedot
Luento 2 [group details and registration]; registered 54, maximum 100
reg.time: 1.8.2017 00:00 - 30.9.2017 23:59
 LocationWeekDayDateAtSupervisorFurther informationURITapahtuman tiedot
1Ag C23236Fr8.9.201712:15-14:00TerziyanTapahtuman tiedot
2Ag C23238Fr22.9.201712:15-14:00TerziyanTapahtuman tiedot
3Ag C23239Fr29.9.201712:15-14:00TerziyanTapahtuman tiedot
4Ag D214.140Fr6.10.201712:15-14:00TerziyanTapahtuman tiedot
5Ag C23241Fr13.10.201712:15-14:00TerziyanTapahtuman tiedot