Advanced Topics in Data Analytics and Machine Learning
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Teaching
Details
Faculty Faculty of Management, Economics and Social Sciences Domain Economics Code UE-EEP.00517 Languages English Type of lesson Seminar
Level Master Semester SA-2024 Schedules and rooms
Summary schedule Wednesday 08:15 - 17:00, Cours bloc, PER 21, Room C230 (Autumn semester)
Thursday 08:15 - 17:00, Cours bloc, PER 21, Room C230 (Autumn semester)
Friday 08:15 - 17:00, Cours bloc, PER 21, Room E040 (Autumn semester)
Hours per week 1 Teaching
Responsibles - Huber Martin
Teachers - Huber Martin
Description Deep Learning with Python - from tabular to multimedia (taught by Dr. Christian Kauth)
Deep learning with neuralnetworks is a fascinating field, especially on non-tabular data (like images and text). The mixture of faster hardware, new techniques, highly optimized open source libraries and large datasets allow very large networks to be created with frightening ease. Deep neural networks have repeatedly proven impressively skillful on a range of problems.
This course is a guide to deep learning in Python. You will discover the Keras Python library for deep learningandhowtouseittodevelopandevaluatedeeplearningmodels.Youwilldiscoverthetechniques and develop the skills in deep learning that you can then bring to your own machine learning projects.
After familiarizing with Keras, we will illustrate the skill of deep learning on some well-understood case study machine learning problems from the UCI Machine learning repository (http://archive.ics.uci.edu/ml/index.php)andcomparetheperformancetotheclassicalmachinelearning approaches used in the course “Machine Learning with Python – from Zero to Hero”. Next we introduce convolutional layers to the networks and use them to classify handwritten digits (e.g. MNIST dataset http://yann.lecun.com/exdb/mnist/) and real-world objects (e.g. CIFAR-10https://www.cs.toronto.edu/~kriz/cifar.html).
We then shift our focus from images to text data, and learn how to solve common Natural Language Processing (NLP) tasks such as text classification, token classification, summarization and translation. To that purpose, we’ll use state-of-the-art transformer-based pre-trained models and see how to fine-tune them on our own dataset (transfer-learning).
Lastly you’ll learn how to integratethe temporal dimension into your machine learning projects to make forecasts on time-series. We’ll compare the performance of several techniques, reaching from tabular representation to state-of-the-art causal transformer architectures.
Fun fact: Deep learning is taking AI performance in compute vision, natural language and time series analysis from deceptive to disruptive, and the Attention mechanism plays a crucial role in this success story. If you’re curious, have a look at the paper “Attention is all you need” https://arxiv.org/abs/1706.0376
Training objectives - To understand the structure and working principles of neural networks and transformers
- To gain insights into some layer types of feed-forward neural networks (dense, convolutional, dropout, attention, embedding) and how they are trained.
- To learn how to classify images with neural networks
- To learn how to solve common NLP tasks with transformers
- To learn how to frametime-series tasks for machine and deep learning
- To gain hands-on experience with Python and the deep learning library Keras
- To be able to leverage and fine-tune state-of-the-art models (from Microsoft, Open AI, Google, Deep Mind, Hugging Face)
Condition of access - Fluency in the programming language "Python", as e.g. provider in the course "machine Learning with Python - from Zero to Hero"
- Google account to access Google Colab
Softskills No Off field No BeNeFri No Mobility No UniPop No -
Dates and rooms
Date Hour Type of lesson Place 12.02.2025 08:15 - 17:00 Cours PER 21, Room C230 13.02.2025 08:15 - 17:00 Cours PER 21, Room C230 14.02.2025 08:15 - 17:00 Cours PER 21, Room E040 -
Assessments methods
Evaluation continue - SA-2024, Session d'hiver 2025
Assessments methods By rating Descriptions of Exams Take home exam: project work to be solved in Python on image, text and time-series data
Course with continuous evaluation: after the registration period, you can no longer cancel your registration (see session calendar on the Faculty's website).
No retake exam
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Assignment
Valid for the following curricula: Ma - Accounting and Finance - 120 ECTS
Version: 2024/SP_V01_DD_Caen
UniFr courses > Elective courses - Max 18 ECTS > SES Master level courses
Ma - Accounting and Finance - 90 ECTS
Version: 2021/SA_V01 Dès SA-2024
Course - 72 ECTS > Minimum 0 / maximum 1 optional master course offered at the University of Fribourg, if 72 ECTS not yet reached in the above modules > SES Master level courses
Ma - Business Communication : Business Informatics - 90 ECTS
Version: 2020/SA_V02
Courses - 60 ECTS > Option Group > Information Management > Cours > Module Informatik > Data Science
Ma - Business Informatics - 90 ECTS
Version: 2020/SA-v01
Classes - min. 45 ECTS > Module IT and IT Management > Data Science
Ma - Communication and Media Research - 90 ECTS
Version: 2015/SA_V01
Courses - 60 ECTS > Inter- and Transdisciplinary Perspectives > SES Master level courses
Ma - Communication and Society - 90 ECTS
Version: 2021/SA_V03
Forschungsbereiche > Inter- & Transdisciplinary Perspectives
Ma - Data Analytics & Economics - 90 ECTS
Version: 2020/SA-v01
Courses min 63 ECTS > Mandatory Modules (45 to 63 ECTS) > Module I: Data Analytics (Data)
Ma - Economics - 90 ECTS
Version: 2021/SA_V04
Le choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Sustainable Development and Social Responsibility > Elective courses in Sustainable Development and Social Responsibility > Elective courses in EconomicsLe choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Sustainable Development and Social Responsibility > Elective courses in Sustainable Development and Social Responsibility > Elective courses of the SES Faculty - max. 15 ECTS > SES Master level coursesLe choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Public Economics and Policy > Elective courses in Public Economics and Policy > Elective courses in EconomicsLe choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Public Economics and Policy > Elective courses in Public Economics and Policy > Elective courses of the SES Faculty - max. 15 ECTS > SES Master level coursesLe choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Business Economics > Elective courses in Business Economics > Elective courses in EconomicsLe choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Business Economics > Elective courses in Business Economics > Wahlkurse der SES-Fakultät - max. 15 ECTS > SES Master level coursesLe choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Quantitative Economics > Elective courses in Quantitative EconomicsLe choix de l'option se fait par l'inscription au premier cours dans l'une des options possibles. > Quantitative Economics > Elective courses in Quantitative Economics > Courses from the SES faculty - max. 15 ECTS > SES Master level coursesCourse selection for the Master WITHOUT options > Elective courses > Elective courses in EconomicsCourse selection for the Master WITHOUT options > Elective courses > Elective courses of the SES Faculty - max. 15 ECTS > SES Master level courses
Ma - European Business - 90 ECTS
Version: 2017/SA_v01
Courses - 63 ECTS > Additional courses: Any Master courses of the Faculty of Economics and Social Sciences, as well as maximum 9 ECTS from all Master programmes of the University. > SES Master level courses
Ma - International and European Business - 90 ECTS
Version: 2021/SA_v01 dès SA-2024
Courses > Additional courses > SES Master level courses
Ma - Management - 90 ECTS
Version: 2021/SA_v03 dès SA-2024
Courses: min. 72 ECTS > Elective courses > SES Master level courses
Ma - Management - 90 ECTS [MA]
Version: 2017/SA_v01
Courses: min. 63 ECTS > Cours facultatifs : max. 18 ECTS > SES Master level courses
Ma - Marketing - 90 ECTS
Version: 2021/V03 dès SA-2024
Courses - 72 ECTS > Elective Master courses from the whole university > SES Master level courses
Ma - Public Economics and Public Finance - 90 ECTS
Version: 2021/SA_V01_DD_PEPF
Cours > Up to 40 ECTS credits must fulfill the conditions required for the specialisation according to the approuved document "Individual choice of lectures". > Elective courses in Economics
MiMa - Business Informatics - 30 ECTS
Version: 2020/SA_V01
Cours > Module Informatik > Data Science
MiMa - Data Analytics - 30 ECTS
Version: 2020/SA-v01
À choix 9 crédits ECTS > Data Science
MiMa - Economics - 30 ECTS
Version: 2021/SA_V01
Elective courses > Elective courses in Economics