Machine Learning Coffee Seminars

About Us

Machine Learning Coffee seminars are weekly seminars held jointly by the Aalto University and the University of Helsinki. The seminars aim to gather people from different fields of science with interest in machine learning. Seminars will be held weekly on Mondays at 9 am - 10 am. The location alternates between Aalto University and the University of Helsinki. At Aalto University, talks will be held in Konemiehentie 2, seminar room T5 and at the University of Helsinki in Kumpula, seminar room Exactum D123 (Gustaf Hällströmin katu 2b), unless otherwise noted. Talks will begin at 9:15 am and coffee will be served from 9:00 am.

Subscribe to our mailing list where seminar topics are announced beforehand.

 

Upcoming Talks (2018)

Give a talk

If you are interested in giving a talk, feel free to send your suggested topic and abstract to mlseminar@hiit.fi

Spring 2018


Feb. 26

Bayesian Deep Learning for Image Data

Abstract: Deep learning is the paradigm that lies at the heart of state-of-the-art machine learning approaches. Despite their groundbreaking success on a wide range of applications, deep neural nets suffer from: i) being severely prone to overfitting, ii) requiring intensive handcrafting in topology design, iii) being agnostic to model uncertainty, iv) and demanding large volumes of labeled data. The Bayesian approach provides principled solutions to all of these problems. Bayesian deep learning converts the loss minimization problem of conventional neural nets into a posterior inference problem by assigning prior distributions on synaptic weights. This talk will provide a recap of recent advances in Bayesian neural net inference and detail my contributions to the solution of this problem. I will demonstrate how Bayesian neural nets can achieve groundbreaking performance in weakly-supervised learning, active learning, few-shot learning, and transfer learning setups when applied to medical image analysis and core computer vision tasks. I will conclude by a summary of my ongoing research in reinforcement active learning, video-based imitation learning, and reconciliation of Bayesian Program Learning with Generative Adversarial Nets.

Speaker: Melih Kandemir

Dr. Kandemir studied computer science in Hacettepe University and Bilkent University between 2001 and 2008. Later on, he pursued his doctoral studies in Aalto University (former Helsinki University of Technology) on the development of machine learning models for mental state inference until 2013. He worked as a postdoctoral researcher in Heidelberg University, Heidelberg Collaboratory for Image Processing (HCI) between 2013 and 2016. As of 2017, he is an assistant professor at Özyeğin University, Computer Science Department. Throughout his career, he took part in various research projects in funded collaboration with multinational corporations including Nokia, Robert Bosch GmbH, and Carl Zeiss AG. Bayesian deep learning, few-shot learning, active learning, reinforcement learning, and application of these approaches to computer vision are among his research interests.

Affiliation: Professor of Computer Science, Özyeğin University

Place of Seminar: Aalto University


March 5

Abstract:

Speaker: Tuuli Toivonen

Affiliation: Professor of Geoinformatics, University of Helsinki

Place of Seminar: University of Helsinki

 

History of Previous Talks

 

Organizers


Samuel Kaski  Professor of Computer Science, Aalto University
Teemu Roos Associate Professor of Computer Science, University of Helsinki
Homayun Afrabandpey PhD Student, Aalto University

 


Last updated on 20 Feb 2018 by Homayun Afrabandpey - Page created on 3 Dec 2016 by Homayun Afrabandpey