TIES4910 Deep-Learning for Cognitive Computing, Theory (5 cr)

Content

The course ("Deep Learning for Cognitive Computing. Theory") concerns the so called Bottom-Up (“Statistical”) approach to AI, when AI can be trained on the basis of available data. This includes various aspects of Machine Learning (Supervised Learning, Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning, Adversarial Learning, Deep Learning, etc.). Cognitive Computing part is represented by a variety of deep neural network architectures (including recurrent, convolutional, adversarial, etc.) suitable for processing and translating natural language texts, speech-to-text and text-to speech transformations, speech recognition, tone (emotional and other) analysis on the basis of texts, capturing cognitive profiles of people, recognizing and tagging patterns from images and videos, generating new texts, handwriting, speech, images (including art), etc.

The course Deep Learning for Cognitive Computing is an evolution of some former "cognitive computing"-related courses starting from 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 "Deep Learning for Cognitive Computing. Theory" 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.

Completion methods

Lectures and Assignment

Assessment details

Research maturity, integrity and quality of the assignment

Knowledge on Deep Learning and Cognitive Computing domain and available related services; Understanding of the Deep Learning "philosophy";
Knowledge on Neural Nets and Deep Neural Nets (Recurrent, Convolutional, Adversarial, Transformers, etc.) and their learning approaches; Capability to utilize available online Cognitive Computing services as an advanced user

Available on-line https://ai.it.jyu.fi/vagan/DL4CC.html