Advanced AI and machine learning

In-depth understanding and utilisation of machine learning and artificial intelligence in different business areas requires knowledge of both the business area and the specific methods developed for it.

Machine learning and artificial intelligence methods have been developed for various industries, such as medicine, speech processing, speech recognition, machine vision and energy technology applications.

During the advanced AI courses of FITech universities, you will be familiarised with concepts such as data gathering, data preprocessing and normalisation, missing data and outliers.

Courses run through different aspects of modern intelligent methods for data processing. Some of the courses focus especially on classifiers and classification.

Courses present the concept of “big data” and different phenomena related to it, including requirements and principles for data. Courses also provide an elementary hands-on introduction to deep learning.

NB! Some courses have limits on the amount of FITech students. Persons without a valid study right to a Finnish university have preference to those courses.

Advanced AI and machine learning courses:

= Contact learning
= Online learning
= Blended learning (online & contact learning)
University of Oulu: Towards data mining, 5 ECTS. 2.9.–25.10.2019.

Application period has ended.

Course code: 521156S

Max amount of FITech students: 30

Course level: Advanced

Language: Finnish or English

Prerequisites: Basics of statistical mathematics.

Amount and quality of data plays a major role in modern machine learning applications. In this course the students are familiarised with different concepts concerning data such as: data gathering, data pre-processing and normalisation, combining data from multiple sources, missing data and outliers.

After this course the student has good capabilities to identify use cases for data that has been gathered in industrial environment or organise high-quality data gathering for new applications.

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Responsible teacher: Satu Tamminen (satu.tamminen(at)oulu.fi)

Aalto University: Speech processing, 5 ECTS. 9.9.–21.10.2019.

Application period has ended.

Course code: ELEC-E5500

Max amount of FITech students: 10

Course level: Advanced

Language: English, can be passed in Finnish or Swedish on request

Course content:

  • Basics of audio signal processing
  • Basics of psychoacoustics and perceptually motivated signal processing
  • Acoustic theory of speech production
  • The functions, acoustics and modelling of the larynx and the vocal tract
  • Phones and phonation
  • Time-frequency analysis of speech signals
  • Principles of speech coding
  • Linear prediction and its application in processing of speech signals
  • Speech synthesis and speech recognition

Responsible teacher: Tom Bäckström (tom.backstrom(at)aalto.fi)

Aalto University: Bayesian data analysis, 5 ECTS. 9.9.–5.12.2019.

Application period has ended.

Course code: CS-E5710

Max amount of FITech students: 100

Course level: Advanced

Language: English

Prerequisites: Differential and integral calculus, basics of probability and statistics, basics of programming (R or Python). Recommended: matrix algebra.

Course content:

  • Bayesian probability theory and bayesian inference
  • Bayesian models and their analysis
  • Computational methods, Markov-Chain Monte Carlo

After the course, the student

  • can explain the central concepts in Bayesian statistics and name steps of the Bayesian modeling process
  • can recognise usages for common (i.e. those presented during the course) statistical models, and formulate the models in these situations
  • can compare the most popular Bayesian simulation methods and implement them
  • can use analytic and simulation based methods for learning the parameters of a given model
  • can estimate the fit of a model to data and compare models.

More information on Aalto Unversity’s WebOodi page.

Responsible teacher: Aki Vehtari (aki.vehtari(at)aalto.fi)

Aalto University: Computer vision, 5 ECTS. 9.9.–13.12.2019.

Application period has ended.

Course code: CS-E4850

Max amount of FITech students: 20

Course level: Advanced

Language: English

Prerequisites: Programming skills and basic knowledge of data structures and mathematics (linear algebra, probability) are necessary. Matlab is used in the programming exercises and therefore previous experience with Matlab is beneficial.

Course content:

  • Image formation and processing
  • Feature detection and matching
  • Motion estimation
  • Structure-from-motion
  • Object recognition
  • Image-based 3D reconstruction

The course gives an overview of algorithms, models and methods which are used in automatic analysis of visual data.

After the course, the student

  • is familiar with basic concepts and methods of computer vision
  • understands the basic principles of image-based 3D reconstruction
  • is familiar with techniques used for automatic object recognition from images
  • can design and implement common computer vision methods and apply them to practical problems with real-world image data.

More information on Aalto University’s WebOodi page.

Responsible teacher: Juho Kannala (juho.kannala(at)aalto.fi)

Aalto University: Machine learning – Basic principles, 5 ECTS. 10.9.–24.10.2019.

Application period has ended.

Course code: CS-C3210

Max amount of FITech students: 40

Course level: Advanced

Language: English

Prerequisites: Basic knowledge of data science (e.g. course Data science CS-C3160).

The course deals with basic principles needed to understand and apply machine learning models and methods. The topics include supervised and unsupervised learning, Bayesian decision theory, parametric methods, tuning model complexity, dimensionality reduction, clustering, nonparametric methods, decision trees, comparing and combining algorithms, as well as a few applications of these methods.

After the course, the student is able to apply the basic machine learning methods to data and to understand new models based on these principles.

More information on Aalto University’s WebOodi page.

Responsible teacher: Juho Rousu (juho.rousu(at)aalto.fi)

University of Oulu: Deep learning, 5 ECTS. 28.10.–12.12.2019.

Apply before Oct 21, 2019

Course code: 521152S

Max amount of FITech students: 30

Course level: Advanced

Language: English

Prerequisites: BSc in Computer science or equivalent.

This course provides an elementary hands-on introduction to deep learning. Students taking this course will learn the theories, models, algorithms, implementation and recent progress of deep learning and obtain empirical experience on training deep neural networks. Applications of deep learning to typical computer vision problems such as object detection and segmentation will also be included.

Coursework will consist of programming assignments in TensorFlow. After this course, students will learn to implement, train and debug their own neural networks.

Responsible teacher: Li Liu (li.liu(at)oulu.fi)

Aalto University: Data science, 5 ECTS. 28.10.–16.12.2019.

Apply before Oct 21, 2019

Course code: CS-C3160

Max amount of FITech students: 100

Course level: Advanced

Language: English

Prerequisites: Skills needed on the course are taught on introductory courses in mathematics, statistics and programming. Specifically, matrix algebra, derivatives of functions and statistical distributions will be needed on this course.

The course serves as an introduction to the topic of data science and related topics such as machine learning. You will be introduced to data science methods and tools to find interesting information from data.

Specific topics on the course include

  • processing of digital signals such as speech and images
  • statistical estimation of parametric distributions
  • classification
  • prediction
  • clustering
  • pattern mining
  • network analysis for developing search engines for hypertext collections such as the web.

After the course, you can describe how natural data such as images, natural language, speech and time series measurements can be represented as data in digital form. You can apply elementary statistical and algorithmic methods to process the digital data to yield insights to the data generating phenomenon. You will understand what processes constitute the data science pipeline in the analysis, starting from natural data and ending with actionable results.

More information on Aalto Unversity’s WebOodi page.

Responsible teacher: Jaakko Hollmen (jaakko.hollmen(at)aalto.fi)

Aalto University: Speech recognition, 5 ECTS. 30.10.–13.12.2019.

Apply before Oct 23, 2019

Course code: ELEC-E5510

Max amount of FITech students: 10

Course level: Advanced

Language: English

Prerequisites: Basic engineering mathematics and probability consept.

Course content:

  • Preprocessing and feature extraction for speech phoneme models
  • Decoding
  • Lexicon and language models
  • Recognition and retrieval of continuous speech

After completing the course you will have become familiar with speech recognition methods and applications. Additionally, you will have learned to understand the structure of a typical speech recognition system and to know how to construct one in practice.

More information on Aalto Unversity’s WebOodi page.

Responsible teacher: Mikko Kurimo (mikko.kurimo(at)aalto.fi)

University of Oulu: Machine learning, 5 ECTS. 7.1.–27.2.2020.

Apply before Dec 16, 2019

Course code: 521289S

Max amount of FITech students: 30

Course level: Advanced

Language: English, examination can be taken in English or Finnish

Prerequisites: BSc in Computer science or equivalent.

On this course, the focus is especially on classifiers and classification. Other topics covered include statistical regression, Bayesian decision theory and feature extraction.

After completing this course the student is able to design basic classifiers for different classification tasks as well as assess the performance of these classifiers.

More info >>

Responsible teacher: Tapio Seppänen (tapio.seppanen(at)oulu.fi)

University of Oulu: Artificial intelligence, 5 ECTS. 8.1.–20.2.2020.

Apply before Dec 16, 2019

Course code: 521495A

Max amount of FITech students: 30

Course level: Advanced

Language: English

Prerequisites: BSc in Computer science or equivalent.

Artificial intelligence course runs through different aspects of modern intelligent methods for data processing. Course topics include among others intelligent agents, search strategies, reinforcement learning and machine learning from observations.

This course gives the student acquirements to start designing various types of artificial intelligence based solutions for real life problems.

More info >>

Responsible teacher: Abdenour Hadid (abdenour.hadid(at)oulu.fi)

University of Vaasa: Artificial intelligence in energy technology, 5 ECTS. 9.1.–17.3.2020.

Apply before Dec 16, 2019

Course code: ICAT2090

Max amount of FITech students: 15

Course level: Intermediate

Language: English or Finnish

Prerequisites: It is highly recommended to know basics of programming. Also some knowledge on object-oriented programming is recommended.

On this course, students will learn the principles of fuzzy logic, rules and control, evolutionary computation, multiparameter and global optimisation as well as basics of neural networks. The idea is to learn how to apply these theoretical methods to energy applications. Content also includes different soft computing methods and designing, implementing and testing simple soft computing applications.

More information on University of Vaasa’s WebOodi page.

Responsible teacher: Jarmo Alander (jarmo.alander(at)univaasa.fi)

University of Vaasa: Machine learning, 5 ECTS. 20.1.–12.3.2020.

Apply before Jan 13, 2020

Course code: ICAT3120

Max amount of FITech students: 15

Course level: Advanced

Language: English

Prerequisites: It is highly recommended to know fundamentals of probability theory and university level calculus.

NB! The course is organised once a year in spring. However, it is possible to arrange the course also as self-study with few hours of online meetings. For self-studying, the course could be arranged also in autumn.

The main concepts as well as the different types of machine learning are covered on this course. The approach of this course is to cover machine learning from algorithmic point of view. The aim of this approach is to understand the theories/algorithms behind machine learning algorithms and how to select the best one for our specific problem, to know their limits, and even how to modify it to fit our specific problem

This course is highly useful wherever there is data to be analysed. Hence, the application area is huge either in industry, factories, power plants, social science, business, finance, etc.

It is important that the student has good mathematical background as well as some programming skills (in any programming language) in order to to maximise the gained knowledge.

More information on University of Vaasa’s WebOodi page.

Responsible teacher: Mohammed Elmusrati (mohammed.elmusrati(at)univaasa.fi)

Further information:

University of Oulu

Riku Hietaniemi (riku.hietaniemi(at)oulu.fi)

Aalto University

Minna Kivihalme (minna.kivihalme(at)aalto.fi)

University of Vaasa

Maria Tuuri (maria.tuuri(at)univaasa.fi)

Contact person, applications:

Pilvi Lempiäinen (pilvi.lempiainen(at)fitech.io)

Type of study unit

Set of courses

Teaching semester

2019–2020

Host university

Aalto University, University of Oulu, University of Vaasa

Open for degree student

Yes

Open for non-student

Yes

Level of studies

Advanced

Teaching methods

Contact or blended learning

Place of contact learning

Espoo, Vaasa, Oulu

Language

English

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