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Course Outline
Invoering
Kansrekening, modelselectie, beslissings- en informatietheorie
Waarschijnlijkheidsverdelingen
Lineaire modellen voor regressie en classificatie
Neural Networks
Kernel-methoden
Schaarse kernelmachines
Grafische modellen
Mengmodellen en EM
Geschatte gevolgtrekking
Bemonsteringsmethoden
Continue latente variabelen
Sequentiële gegevens
Modellen combineren
Samenvatting en conclusie
Requirements
- Inzicht in statistieken.
- Bekendheid met multivariate calculus en elementaire lineaire algebra.
- Enige ervaring met waarschijnlijkheden.
Publiek
- Data-analisten
- Promovendi, onderzoekers en praktijkmensen
21 Hours
Getuigenissen (3)
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Cursus - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Cursus - Applied AI from Scratch in Python
Very flexible