Канал: Alexander Jung

An Introduction to Heuristic Algorithms

An Introduction to Heuristic Algorithms

Forecasting Retail Investor Fund Flow Using Machine Learning

Forecasting Retail Investor Fund Flow Using Machine Learning

Good Woodlife in #Kustavi

Good Woodlife in #Kustavi

Determining User Preference Profiles from Email And User Engagement Data

Determining User Preference Profiles from Email And User Engagement Data

Machine Learning Kinship Verification

Machine Learning Kinship Verification

Object Detection for Security Systems

Object Detection for Security Systems

LLM for Mental Health

LLM for Mental Health

Backdoor Attacks on Transformer-Based Weather Forecast

Backdoor Attacks on Transformer-Based Weather Forecast

Traffic Accident Forecasting with Deep Learning

Traffic Accident Forecasting with Deep Learning

Trustworthy Object Detection

Trustworthy Object Detection

CS-E4740 Data and Model Poisoning

CS-E4740 Data and Model Poisoning

CS-E4740 Privacy Protection in FL

CS-E4740 Privacy Protection in FL

CS E4740 FL at Nokia Part II

CS E4740 FL at Nokia Part II

CS-E4740 Federated Learning at Nokia - Part I

CS-E4740 Federated Learning at Nokia - Part I

CS-E4740 Privacy-Preserving Data Processing

CS-E4740 Privacy-Preserving Data Processing

CS-E4740 Explainable Federated Learning

CS-E4740 Explainable Federated Learning

CS-E4740 Human Agency and Oversight in Federated Learning

CS-E4740 Human Agency and Oversight in Federated Learning

Humans must regulate Technology

Humans must regulate Technology

CS E4740 From Linear to Non Linear Models

CS E4740 From Linear to Non Linear Models

CS-E4740 Trustworthy FL

CS-E4740 Trustworthy FL

Group Meeting 26-03-2024

Group Meeting 26-03-2024

CS-E4740 Graph Learning

CS-E4740 Graph Learning

How Low Can We Go?

How Low Can We Go?

Basic ML for Complex Systems

Basic ML for Complex Systems

Group Meeting 19032024

Group Meeting 19032024

CS-E4740 Guest Talk from Javier Fernandez-Marques (Flower Labs)

CS-E4740 Guest Talk from Javier Fernandez-Marques (Flower Labs)

FedAvg is Projected Gradient Descent

FedAvg is Projected Gradient Descent

CS-E4740 FL Algorithms

CS-E4740 FL Algorithms

CS E4740 Lecture "Gradient Methods"

CS E4740 Lecture "Gradient Methods"

CS E4740 Gradient Descent for Non-Parametric Models?

CS E4740 Gradient Descent for Non-Parametric Models?

CS-E4740 Lecture "FL Design Principle"

CS-E4740 Lecture "FL Design Principle"

Three Perspectives of Regularization in Machine Learning

Three Perspectives of Regularization in Machine Learning

CS-E4740 "ML Basics"

CS-E4740 "ML Basics"

CS-E4740 ML Basics Part I

CS-E4740 ML Basics Part I

CS-E4740 Introduction

CS-E4740 Introduction

#skiing #helsinki

#skiing #helsinki

Machine Learning for Finnair

Machine Learning for Finnair

Machine Learning Teaching Humans about Machine Learning

Machine Learning Teaching Humans about Machine Learning

Frequency offset compensation using deep learning for 5G mobile networks

Frequency offset compensation using deep learning for 5G mobile networks

Skiing #helsinki

Skiing #helsinki

Explainable Machine Learning

Explainable Machine Learning

Personalized Federated Learning

Personalized Federated Learning

Lawsuit against Meta AI

Lawsuit against Meta AI

Group Meeting 24-Oct-2023

Group Meeting 24-Oct-2023

Group Meeting 17 Oct 2023

Group Meeting 17 Oct 2023

Group Meeting 10 Oct 2023

Group Meeting 10 Oct 2023

@amosrexartmuseum

@amosrexartmuseum

#networks #data @amosrexartmuseum

#networks #data @amosrexartmuseum

#network #data @amosrexartmuseum

#network #data @amosrexartmuseum

@amosrexartmuseum

@amosrexartmuseum

@amosrexartmuseum

@amosrexartmuseum

#museum

#museum

#helsinki

#helsinki

Amos rex

Amos rex

30 September 2023

30 September 2023

Machine Learning for the People

Machine Learning for the People

Software Defined Networking Energy Optimization through Traffic Prediction on Mobile Access Networks

Software Defined Networking Energy Optimization through Traffic Prediction on Mobile Access Networks

Microservices-Based Autonomous Anomaly Detection

Microservices-Based Autonomous Anomaly Detection

Group Meeting 19-Sept-2023

Group Meeting 19-Sept-2023

Group Meeting 12 Sept 2023

Group Meeting 12 Sept 2023

Heterogeneous Federated Learning

Heterogeneous Federated Learning

Group BBQ - 1-Sep-2023

Group BBQ - 1-Sep-2023

Group Meeting 15 Aug 2023

Group Meeting 15 Aug 2023

Group Meeting-8-Aug-23

Group Meeting-8-Aug-23

Machine Learning for Airline Costumer Satisfaction

Machine Learning for Airline Costumer Satisfaction

Machine Learning for Material Capture and Generative Rendering

Machine Learning for Material Capture and Generative Rendering

Machine Learning Formula 1

Machine Learning Formula 1

Machine Learning to Design Electrical Machines

Machine Learning to Design Electrical Machines

#kustavi #blade

#kustavi #blade

#ferry #kustavi

#ferry #kustavi

#ferry #kustavi

#ferry #kustavi

#tree trunk removal

#tree trunk removal

Towards Model Agnostic Federated Learning over Networks

Towards Model Agnostic Federated Learning over Networks

#wachau is the #tuscany of #austria

#wachau is the #tuscany of #austria

#windpower #kustavi

#windpower #kustavi

Wind power in #kustavi

Wind power in #kustavi

9 June 2023

9 June 2023

BIM Connection Recommendation Based on Machine Learning Using Multimodal Information

BIM Connection Recommendation Based on Machine Learning Using Multimodal Information

A Design Principle for Trustworthy Federated Learning

A Design Principle for Trustworthy Federated Learning

CS E4740 Data Poisoning

CS E4740 Data Poisoning

CS E4740 Privacy in FL

CS E4740 Privacy in FL

CS E4740 Trustworthy FL

CS E4740 Trustworthy FL

CS-E4740 Graph Learning

CS-E4740 Graph Learning

How to Write a Federated Learning Paper

How to Write a Federated Learning Paper

A Wind-Power Atlas Service for Finland

A Wind-Power Atlas Service for Finland

Friday Group BBQ in Vuosaari

Friday Group BBQ in Vuosaari

CS-E4740 Coding Assignment "Vertical FL"

CS-E4740 Coding Assignment "Vertical FL"

CS-E4740 FL Flavors

CS-E4740 FL Flavors

Federated Learning Turtlebot Lane Detection

Federated Learning Turtlebot Lane Detection

CS-E4740 FL Algorithms

CS-E4740 FL Algorithms

CS-E4740 FL Design Principle

CS-E4740 FL Design Principle

Evaluating a Hypothesis Map

Evaluating a Hypothesis Map

CS-E4740 Perturbed Gradient Descent

CS-E4740 Perturbed Gradient Descent

CS-E4740 Perfect Linear Fit

CS-E4740 Perfect Linear Fit

CS-E4740 Network Models

CS-E4740 Network Models

CS-E4740 Gradient Methods

CS-E4740 Gradient Methods

CS-E4740 Regularization

CS-E4740 Regularization

Home Office Helsinki.

Home Office Helsinki.

CS-E4740 ML Design Principle

CS-E4740 ML Design Principle

CS-E4740 Welcome and Course Logistics

CS-E4740 Welcome and Course Logistics

Archipelago winter

Archipelago winter

Old meets modern.

Old meets modern.

Deep learning based Mammography Image Segmentation

Deep learning based Mammography Image Segmentation

Classification of Purchase Invoices to Analytic Accounts with Machine Learning

Classification of Purchase Invoices to Analytic Accounts with Machine Learning

#phasetransition #physics #shape #kustavi

#phasetransition #physics #shape #kustavi

#flames

#flames

#waves #flow #helsinki #helsinkifinland

#waves #flow #helsinki #helsinkifinland

Datapoint = A turn

Datapoint = A turn

When to Turn?

When to Turn?

Lift

Lift

Skiing

Skiing

Pallet Detection in Warehouse Environments

Pallet Detection in Warehouse Environments

descent towards #rastila #helsinki

descent towards #rastila #helsinki

#snowfall #helsinkifinland

#snowfall #helsinkifinland

Descent towards Talosaari

Descent towards Talosaari

skiing

skiing

snow

snow

#skiing #helsinki

#skiing #helsinki

Short-term or Long-term reward?

Short-term or Long-term reward?

Transfer Learning to Ski.

Transfer Learning to Ski.

Learning to Carve.

Learning to Carve.

Reinforcement Learning to Fly.

Reinforcement Learning to Fly.

Reinforcement Learning between Vuosaari and Talosaari.

Reinforcement Learning between Vuosaari and Talosaari.

Federated Learning

Federated Learning

Regularization

Regularization

Empirical Risk Minimization

Empirical Risk Minimization

The Machine Learning Landscape

The Machine Learning Landscape

A Blueprint for Trustworthy ML - Loss

A Blueprint for Trustworthy ML - Loss

A Blueprint for Trustworthy ML - Model

A Blueprint for Trustworthy ML - Model

Data

Data

How Do Machines Learn ?

How Do Machines Learn ?

A Blueprint for Trustworthy Machine Learning

A Blueprint for Trustworthy Machine Learning

Trustworthy Machine Learning

Trustworthy Machine Learning

CS-EJ3311 Three Components of Deep Learning

CS-EJ3311 Three Components of Deep Learning

Deep Learning with Python '22 - Course Practicalities

Deep Learning with Python '22 - Course Practicalities

Detecting Distracted Car Driver with Machine Learning

Detecting Distracted Car Driver with Machine Learning

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

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

Dr. Robin Rajamäki - Sparse sensor arrays for active sensing

Dr. Robin Rajamäki - Sparse sensor arrays for active sensing

Generative Adversarial Networks in Python

Generative Adversarial Networks in Python

What are three main components of Machine Learning ?

What are three main components of Machine Learning ?

Diana Pfau - Ethics in Machine Learning

Diana Pfau - Ethics in Machine Learning

Machine Learning for Art Fraud Detection

Machine Learning for Art Fraud Detection

Machine Learning for Diagnosis of Hydraulic Systems

Machine Learning for Diagnosis of Hydraulic Systems

Crown-of-Thorns Starfish Detection

Crown-of-Thorns Starfish Detection

Clustering with scikit-learn

Clustering with scikit-learn

Model Validation, Selection and Regularization

Model Validation, Selection and Regularization

Classification with Python

Classification with Python

Regression with Python

Regression with Python

MA Niko Anttiroiko - Mapping Archaeological Heritage with AI and LiDAR

MA Niko Anttiroiko - Mapping Archaeological Heritage with AI and LiDAR

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

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

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

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

Liveproject "Personalized Diagnosis of Symptoms"

Liveproject "Personalized Diagnosis of Symptoms"

Liveproject "Building Networked Models"

Liveproject "Building Networked Models"

Liveproject "Handling Sensitive Data"

Liveproject "Handling Sensitive Data"

Panel "AI for Geospatial Data"

Panel "AI for Geospatial Data"

CS-C3240 Machine Learning Marketing Strategy

CS-C3240 Machine Learning Marketing Strategy

CS-C3240 Predicting Stephen Curry (NBA)

CS-C3240 Predicting Stephen Curry (NBA)

Explainable Weather Forecasting

Explainable Weather Forecasting

CS-C3240 - Predicting Day-Length

CS-C3240 - Predicting Day-Length

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

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

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

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

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

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

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

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

Prof. Simo Särkkä - Parallel/distributed methods for state-space models

Prof. Simo Särkkä - Parallel/distributed methods for state-space models

Dr. Hamed R. Tavakoli - Compact and Efficient Neural Networks

Dr. Hamed R. Tavakoli - Compact and Efficient Neural Networks

What is Data ?

What is Data ?

School Logistics. Exercises.

School Logistics. Exercises.

Model Validation, Selection and Regularization

Model Validation, Selection and Regularization

Model Training

Model Training

CS-EJ3211 Model Validation and Selection

CS-EJ3211 Model Validation and Selection

CS-C3240 Lecture (Regularization and ML at Helsinki)

CS-C3240 Lecture (Regularization and ML at Helsinki)

CS-EJ3211 Linear Regression - MSE, MAE and Huber

CS-EJ3211 Linear Regression - MSE, MAE and Huber

CS-EJ3211 Three Components of Machine Learning with Python

CS-EJ3211 Three Components of Machine Learning with Python

Python Intro

Python Intro

CS-C3240 - A Glimpse on Model Regularization

CS-C3240 - A Glimpse on Model Regularization

CS-C3240 - Model Validation and Selection

CS-C3240 - Model Validation and Selection

CS-C3240. Empirical Risk Minimization.

CS-C3240. Empirical Risk Minimization.

CS-C3240 Course Logistics. Data-Model-Loss.

CS-C3240 Course Logistics. Data-Model-Loss.

Machine Learning Water Melon

Machine Learning Water Melon

Logistic Regression in Python - Part 2

Logistic Regression in Python - Part 2

Logistic Regression in Python - Part I

Logistic Regression in Python - Part I

Linear and Polynomial Regression with Python

Linear and Polynomial Regression with Python

Livestream von Alexander Jung

Livestream von Alexander Jung

Spectral Clustering

Spectral Clustering

Loading Datapoints in Python using Pandas Library

Loading Datapoints in Python using Pandas Library

Machine Learning to Ski

Machine Learning to Ski

Machine Learning for Taksi Helsinki

Machine Learning for Taksi Helsinki

How to Edit and Submit Coding Assignments

How to Edit and Submit Coding Assignments

Generative Adversarial Networks

Generative Adversarial Networks

Collecting Datapoints

Collecting Datapoints

How to Fetch and Re-Fetch Notebooks

How to Fetch and Re-Fetch Notebooks

Natural Language Processing with Python

Natural Language Processing with Python

Deep Learning for Remote Sensing and GIS

Deep Learning for Remote Sensing and GIS

Data Pipelines in TensorFlow

Data Pipelines in TensorFlow

Regularization - Data Augmentation and Transfer Learning

Regularization - Data Augmentation and Transfer Learning

Decoding Electromagnetic Brain Activity

Decoding Electromagnetic Brain Activity

Convolutional Neural Networks

Convolutional Neural Networks

Aerial Inspection RetinaNet for Land Search and Rescue

Aerial Inspection RetinaNet for Land Search and Rescue

Gradient Based Learning

Gradient Based Learning

Loss is for Tuning. Metric is for Selling Deep Learning.

Loss is for Tuning. Metric is for Selling Deep Learning.