Deep
Learning for Cognitive Computing, Theory
(Course
code: TIES4910) 5 ECTS, Autumn Semester
Instructor:
Vagan Terziyan Email:
vagan.terziyan@jyu.fi
(Find the course and register in SISU system)
Attention:
1-st Lecture: Thursday, 10 September 2026. Time: 12:15 - 14:00. Place: Ag B122.1
(Alfa). (Check also in Moodle)
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COURSE
SCHEDULE (FALL-2026)
THIS COURSE IS INDEPENDENT PART OF OUR ARTIFICIAL
INTELLIGENCE COURSES’ PACKAGE; IT DOES NOT REQUIRE ANY PREQUESITIES AND CAN
BE STUDIED REMOTELY [however, in the remote study option, be aware that, while
course slides are constantly updated, the video records of the lectures could
be outdated in some aspects, and an online access to current in-class lectures
is not provided]
Personal quota from
the course instructor:
“Everything will
be Artificial Intelligence soon, and, instead of being worried about it and restricting
it (which is a bad idea), come to study, develop, use, and promote it further!”
Artificial Intelligence (AI) is a major driver of
economic growth and social progress, if industry, civil society, government,
security, and the public work together to support development of the
technology, with thoughtful attention to its potential and to managing its
risks. One of the most emerging trends within AI nowadays and in observable
future is Cognitive Computing and
its enabler – Deep Learning.
The pair of
courses: this one – TIES4910-Deep
Learning for Cognitive Computing, Theory and its extension – TIES4911-Deep Learning for Cognitive
Computing for Developers (5+5=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 and Deep Learning domains
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 Technologies, Agentic AI, Big Data Analytics, Semantic
Web and Linked Data; Cloud Computing, Internet of Things, etc.);
·
To introduce the major providers of cognitive
computing and deep learning services (Intelligence-as-a-Service) on the market
and show demos of their services (e.g., text, speech, image, video, sentiment,
etc., processing, analysis, recognition, diagnostics, prediction, and even
generation);
·
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 to use available
cognitive services 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 and on its enabler Deep Learning 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).
Important
challenges around the course also include Cybersecurity-related aspects of the
Cognitive Computing, Deep Learning and Collective Intelligence.
Emerging Cognitive Computing services attract huge amounts of users worldwide.
Very recently the new vulnerabilities of Cognitive Computing and of its enabler
Deep Learning have been discovered - the so-called Cognitive Risks for Cybersecurity associated with Cognitive Hack, Data Poisoning, Prompt injection, etc. attacks. Therefore, one interesting topic for self-study
would be on how to handle such risks (based on system’s self-awareness and self-protection) for both human minds and
artificial minds (i.e., risks of Cognitive Hacking of the Collective
Intelligence) to make future smart systems secure.
We will
combine overview lectures, self-study, group-work, theoretical and practical
assignments and exercises trying to find an optimal approach to everyone.
Recommended reading: Goodfellow,
I., Bengio, Y., & Courville, A. (2016). Deep Learning, MIT Press,
787 pp. (http://www.deeplearningbook.org)
Recommended reading: Michael
Nielsen (2017). Neural
Networks and Deep Learning. (http://neuralnetworksanddeeplearning.com/)
Deep Learning Resources (http://deeplearning.net/)
COMPLETE
PACKAGE OF THE SLIDES FOR THE WHOLE COURSE IS HERE
Lectures for the course TIES4910-Deep Learning for Cognitive
Computing, Theory:
·
All the lectures and self-study material in one PowerPoint. (Lectures
1-10):
o
See the lecture slides
(download before viewing, enable external content and switch on speakers).
o
Contents:
-
Deep Learning & Cognitive
Computing Introduction;
-
Introduction to Neural Networks (for
beginners);
-
Neural Networks and Fuzzy Logic;
-
Strengths and Weaknesses of Deep
Learning;
-
Recurrent Neural Networks;
-
Deep Learning beyond Deep Neural
Networks: What else Can be “Deep”?;
-
Explainable AI and Causality;
-
Deep Learning and Topology;
-
Training Neural Networks
(Backpropagation, Overfitting, Regularization, etc.);
-
Competition on the AI/ML-as-a-Service
Market (perspectives, fears, and concerns);
-
Online Cognitive Services (they can
do almost everything for you);
-
Convolutional Neural Networks;
-
Deconvolution and Style Transfer;
-
Semantic Segmentation;
-
Generative Adversarial Networks and
Generative AI;
-
Introduction to Transfer of Learning
and Federated Learning;
-
Brief Introduction to Self-Supervised
Learning;
-
Attention and Transformers in Deep
Learning;
-
Physics-Informed Neural Networks;
-
Short Introduction to Reinforcement
Learning;
-
Future of Deep Learning (robots,
education, medicine, etc.);
-
Deep Learning and Security:
Adversarial Machine Learning;
-
Few old slides as a summary about IBM
Watson;
-
Become Master in AI and collaborate
with us.
·
Additional old slides.
o
Topic: Introduction to Neural Networks
and Deep Learning
o
See some old additional lecture slides
(download before viewing).
·
Additional old slides:
o
Topic: Convolutional Neural
Networks for Image Processing
o
See some old additional lecture slides
(download before viewing).
·
Additional old slides:
o
Topic: Neural Networks with
Memory: Recurrent Neural Networks and LSTM Networks
o
See some old additional lecture slides
(download before viewing).
Recorded Video-Lectures:
NOTE: if you
have possibility to come to the in-class lectures, choose this option, because lecture
content is constantly updated, and the recorded lectures from previous years
could be slightly outdated. Please check for current information in the course
slides and in the description of your assignment (especially important is
privacy notice in the assignment task description) …
COMPLETION MODE: Your grade
will be based on the course Assignment (task for the Assignment
is here; deadline: 15 November).
How to continue? We recommend
also taking the course: TIES4911 “Deep Learning for Cognitive
Computing for Developers”.
Collaborate
with our research group on developing stronger AI!