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IN5490 Advanced Topics in Artificial
Intelligence for Intelligent Systems
Lecture 1 – 2018 Course Introduction
Jim Tørresen
• Lecturer:
– Bruno Castro da Silva – [email protected] (UFRGS)
– Charles Martin – [email protected] (UiO)
– Enrique Garcia Ceja – [email protected] (UiO)
– Zia Uddin – [email protected] (UiO)
– Jim Tørresen – [email protected] (UiO)
– and more
• Lecture time: 27–31 August, 15-19 October, 19-23 November
• Lecture room: ROBIN pause area
• Course web page:
https://www.uio.no/studier/emner/matnat/ifi/IN5490/index.html2
IN5490 Advanced Topics in Artificial
Intelligence for Intelligent SystemsAutumn 2018
Topics to be covered in the course
• Classification
• Recurrent neural networks (RNN)
• Deep re-inforcement learning (DRL)
• Convolutional Neural Networks (CNN)
applied to sensor data analysis
• Mobile robotics
3
Learning outcome
• insight into the new and promising methods used in
artificial intelligence (AI) and machine learning (ML)
• have knowledge about how to apply AI methods to
different kinds of applications
• be able to search for literature outlining state-of-the-
art within a specific research field.
• be able to critically assess scientific papers and be
familiar with how to prepare a scientific paper
• be able to design and conduct experiments using AI
methods, with emphasis on evaluation
• have experience in presenting scientific work 4
Course Layout
• Teaching:
– Lectures and organized group work in selected weeks
– Self study and group work in between sessions
• Grading: Pass/not-pass
• To pass:
– give at least one presentation (one scientific paper)
– prepare one research paper draft within each
project group (will be considered to be submitted to
a conference)
– attend at least 80% of all seminar sessions5
6
9th Joint IEEE International Conference on
Development and Learning and on Epigenetic Robotics
19-22 August 2019, Oslo, Norway
Temp. web page: https://icdlepirob2019.wordpress.com
Call for Papers/Workshop/Tutorials flyer available
Paper review and presentation
• Pick one from our list of papers (add your
name to it, only one student can present a
paper)• https://docs.google.com/spreadsheets/d/1xfIJZyOcUallpB8SF1
4JkpkdNKisC1oa5FSUPpjxwJI/edit#gid=0
• Read and understand the paper content
• Prepare a presentation with:
– Paper title page heading
– Main motivation and idea of method in the paper
– Main results
– Assessment of strengths and weaknesses
• Give a presentation in 10 minutes (Oct or Nov)
7
Session Plan 27–31 AugustSee message at course web page for more details
• Monday 14:15-18:00 Course intro + lecture on
publishing papers + initial project workshop + pizza
• Tuesday 14:15-18:00 2 hours lecture + 2 hours
project workshop (select and plan project)
• Wednesday 14:15-18:00 2 hours lecture + 2 hours
project workshop (work on project)
• Thursday 14:15-18:00 2 hours lecture + 2 hours
project workshop (work on project)
• Friday 14:15-16:00 2 hours lecture8
Topics for this week
• Lectures
• Form groups of 3 persons
• Select/define a project to work on in the
course
• Start planning/working on the project
9
Project Implementation
• Preferred programming language: Python
• Preferred tool: Keras + Tensorflow, Jupyter
Notebook, OpenAi Gym
• Group size: 3 students
• Project proposals: https://docs.google.com/document/d/14LZlb0hmp7j-
LPWvEwFeZpB0xV4y-z-84MduhicWuzg
• Register group here: https://docs.google.com/spreadsheets/d/1aNMQO7KCxcoRqwXmg2
8ehmKHm-lHpsopwpfHobpmAWw/edit#gid=0
• Register group: Tuesday at the lastest
• Register selected project: Wednesday at the latest 10
Biology
applyprinciples
from nature
Applications
roboticsmusichealth
++
Hardware electronics3D-printing prototyping
Robotics and Intelligent Systems
Robotics and Intelligent Systems group
ROBIN
Web page: Google for ”ROBIN IFI”
Creating systems for demanding run-time environments.
Robotics and
Intelligent Systems
research
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Prediction/Forecasting Examples
• Nature and society: natural disasters,
pandemics, demography, population
dynamics and meteorology
• Finance: stock market behaviour
• Sports: outcome of sporting events
• Robotics: More effective operation when
human close by
• Music: Real-time music synthesis together
with real musicians13
Interaction and User Behaviour using
Prediction
14
Why: Responsive and user adapted systems
MECS: Multi-sensor Elderly Care Systems Research Council of Norway grant 247697
Funding: FRINATEK
Research Council of Norway
Goal: Create and evaluate multimodal mobile human supportive
systems that are able to sense, learn and predict future events.
INTROMAT: INtroducing personalized
TReatment Of Mental health problems using
Adaptive Technology (2016-2021)
Goal: Increase access to
mental health services
for common mental health
problems by developing
smartphone technology
which can guide patients.http://intromat.no
Project Manager:
Haukeland Univ. Hospital, Bergen
Funding: IKTPLUSS Lighthouse,
Research Council of Norway
EPEC: Prediction and Coordination for
Robots and Interactive MusicResearch Council of Norway grant 240862.
Goal: Design, implement and evaluate multi-sensor
systems that are able to sense, learn and predict
future actions and events.
What is a Prediction?• Output from a trained supervised model
• Confusing in literature:
– With or without representing any state in time
(temporal data)
– Estimate current or future state of a system
(temporal model)
• A more limited definition (informal speech):
Forecasting
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Temporal and Non-Temporal Data
Temporal data is data that represents a state in
time
• Non-temporal: time-independent data
– Text (a set of words rather than sequence of words)
– Still Images
• Temporal: a consecutive sequence of data
– Text (sequence)
– Audio
– Video
– Music
– Animation
– Human motion
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Temporal and Non-Temporal Models
• Non-temporal: consider each input
vector independently
– E.g. Feed-forward neural networks
– Enrique demo
• Temporal: consider past and current
input vector (e.g. using memory or
multiple inputs)
– E.g. Recurrent Neural Networks
– Kai demo20
x
h1
h2
h3
y x
h1
h2
h3
y
Time Delay Network
21
Machine Learning for Prediction: Training Temporal and Non-Temporal Models
• Non-temporal Classification/Recognition• Feed forward neural networks• Convolutional neural networks • Random forest classifier • SVM, K-NN
• Temporal Sequences Prediction• Recurrent neural networks (RNN)
• Long short term memory (LSTM)• Hidden Markov Models• Conditional Random Fields • Dynamic Time Warping
Machine Learning for Prediction: Training Temporal and Non-Temporal Models