Computer Vision and Interpretability

Course Description

The course aims to present basic knowledge of modern approaches such as deep convolutional neural networks for solving computer vision problems. The course will be focused on the tasks of object and action recognition, semantic segmentation, style transfer to name a few. We will cover image and video data types. The interpretability part will touch upon a challenging problem of how to interpret/explain the decisions of neural network systems.

Course topics

Deep convolutional neural networks, object recognition, transparency, interpretability, trust.

Course tools

Python, Caffe, Tensorflow.

Prerequisites

Basic linear algebra, proficiency in Python, machine learning basics (understanding of different types of learning (supervised, unsupervised learning), classification, regression problems, generalization error, overfitting, train/test datasets split). Optional, but desirable: neural network (NN) basics, feed forward NN, different activation functions, backpropagation.

 

Lecturers

Viktoriia Sharmanska

Dr. Viktoriia Sharmanska

Imperial College London, United Kingdom

Viktoriia is passionate about designing intelligent systems that can learn concepts from visual data using machine learning models. She got her PhD in Computer Vision and Machine Learning from IST Austria, and MSc in Applied Mathematics from Taras Shevchenko National University of Kyiv, Ukraine. Since 2015, she’s a visiting research fellow at the University of Sussex, UK working on cross modal learning with privileged information. In October 2017, she joined Imperial College London as a research fellow, where she’s working on action and emotion recognition in video data using deep learning.

Computer Vision and Machine Learning.

Contacts[email protected]
linkedin.com/in/viktoriiasharmanska/

Novi Quadrianto

Dr. Novi Quadrianto

Senior Lecturer at the University of Sussex, United Kingdom

Novi is a co-founder and co-director of Predictive Analytics Lab (PAL) at University of Sussex, Brighton, UK. In addition to undertaking high quality research and publishing in top machine learning and computer vision conferences/journals including NIPS, ICML, CVPR, ICCV/ECCV, JMLR, and TPAMI, the PAL group also creates significant impact by providing support, technology, and highly-trained specialists to a new generation of technology companies. Prior to Sussex, Novi was a Newton International Fellow of the Royal Society at University of Cambridge, UK. He is an Associate Editor of TPAMI and an Area Chair of NIPS 2017 and 2015.

Ethical Machine Learning.

Contacts[email protected]