Intelligence for Intelligent Systems Lecture 1 2018 Course - - PowerPoint PPT Presentation

IN5490 Advanced Topics in Artificial Intelligence for Intelligent Systems Lecture 1 – 2018 Course Introduction Jim Tørresen

• Lecturer:

– Bruno Castro da Silva – bruno.silva@inf.ufrgs.br (UFRGS) – Charles Martin – charlepm@ifi.uio.no (UiO) – Enrique Garcia Ceja – enriqug@ifi.uio.no (UiO) – Zia Uddin – mdzu@ifi.uio.no (UiO) – Jim Tørresen – jimtoer@ifi.uio.no (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.html

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IN5490 Advanced Topics in Artificial Intelligence for Intelligent Systems

Autumn 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

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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

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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 sessions

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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)

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Session Plan 27–31 August

See 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 lecture

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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

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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

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Biology apply principles from nature Applications robotics music health ++ Hardware electronics 3D-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 musicians

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Interaction and User Behaviour using Prediction

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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 Music

Research 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 demo

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x h1 h2 h3 y x h1 h2 h3 y

Time Delay Network

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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