47:41

How to Write a Federated Learning Paper

02:28

A Wind-Power Atlas Service for Finland

08:13

CS-E4740 Coding Assignment "Vertical FL"

46:41

CS-E4740 FL Flavors

02:22

Federated Learning Turtlebot Lane Detection

01:12:29

CS-E4740 FL Algorithms

45:38

CS-E4740 FL Design Principle

15:01

CS-E4740 Perturbed Gradient Descent

20:27

CS-E4740 Perfect Linear Fit

01:03:00

CS-E4740 Network Models

01:12:31

CS-E4740 Gradient Methods

50:24

CS-E4740 Regularization

01:00:45

CS-E4740 ML Design Principle

50:40

CS-E4740 Welcome and Course Logistics

00:07

Old meets modern.

29:03

Deep learning based Mammography Image Segmentation

15:26

Classification of Purchase Invoices to Analytic Accounts with Machine Learning

00:13

When to Turn?

21:53

Pallet Detection in Warehouse Environments

00:14

Transfer Learning to Ski.

06:20

Federated Learning

08:30

Regularization

06:44

Empirical Risk Minimization

06:33

The Machine Learning Landscape

10:05

A Blueprint for Trustworthy ML - Loss

06:58

A Blueprint for Trustworthy ML - Model

05:12

Data

04:05

How Do Machines Learn ?

02:07

A Blueprint for Trustworthy Machine Learning

01:26:54

Trustworthy Machine Learning

01:18:47

CS-EJ3311 Three Components of Deep Learning

23:13

Deep Learning with Python '22 - Course Practicalities

12:37

Detecting Distracted Car Driver with Machine Learning

36:28

Dr. Zitong Yu - Facial-Video-Based Physiological Signal Measurement

43:20

Dr. Robin Rajamäki - Sparse sensor arrays for active sensing

25:36

Generative Adversarial Networks in Python

00:21

What are three main components of Machine Learning ?

51:32

Diana Pfau - Ethics in Machine Learning

19:20

Machine Learning for Art Fraud Detection

21:12

Machine Learning for Diagnosis of Hydraulic Systems

12:29

Crown-of-Thorns Starfish Detection

29:30

Clustering with scikit-learn

19:24

Model Validation, Selection and Regularization

41:29

Classification with Python

19:12

Regression with Python

18:44

MA Niko Anttiroiko - Mapping Archaeological Heritage with AI and LiDAR

14:53

M.Sc. (Tech.) Christian Koski - Mapping small streams and ditches with deep learning

21:05

Dr. Lingli Zhu - Introduction to the ATMU Project of National Land Survey Finland

00:35

Liveproject "Personalized Diagnosis of Symptoms"

00:30

Liveproject "Building Networked Models"

00:29

Liveproject "Handling Sensitive Data"

26:53

Panel "AI for Geospatial Data"

04:44

CS-C3240 Machine Learning Marketing Strategy

06:16

CS-C3240 Predicting Stephen Curry (NBA)

11:34

Explainable Weather Forecasting

05:09

CS-C3240 - Predicting Day-Length

59:08

DI Anna Saranti, Tackling the problem of “bad” explanations with the Human-in-the-Loop principle

01:16:36

Prof. Carlo Fischione - Communication-Computation Efficient Distributed Machine Learning

01:01:03

Dr. Wojciech Samek - Towards Communication-Efficient and Personalized Federated Learning

01:08:14

Dr. Irene Schicker (ZAMG) - Machine Learning applications in meteorological forecasting