BibSonomy bookmarks for /user/analyst/course

Courses taught by Bala Krishnamoorthy

analyst — 2022-02-16T07:58:40+01:00

WSU

Computer Graphics - Spring 2020 @ GIST

analyst — 2022-01-05T20:36:55+01:00

CGLab (since 2016.09) focuses on conducting research on photorealistic rendering, which includes a variety of optimization techniques for ray tracing. The main applications of photorealistic rendering are CG movies, animations, 3D games and immersive technology (AR and VR).

6.874 Computational Systems Biology: Deep Learning in the Life Sciences - Spring 2021

analyst — 2021-07-16T10:40:31+02:00

Course materials and notes for MIT class 6.802 / 6.874 / 20.390 / 20.490 / HST.506 Computational Systems Biology: Deep Learning in the Life Sciences

CS590: Cloud-Native Database Systems | Purdue

analyst — 2021-07-05T08:38:16+02:00

https://www.cs.purdue.edu/homes/csjgwang/cloudb/

Designing Products of the Future (Cecilia Tham). Online Course | Domestika

analyst — 2021-06-25T17:08:11+02:00

https://www.domestika.org/en/courses/1880-designing-products-of-the-future

AM 207 - Stochastic Methods for Data Analysis, Inference, and Optimization

analyst — 2021-04-28T06:43:53+02:00

https://xuwd11.github.io/am207/material.html

Computational Learning Theory - Michaelmas Term 2018

analyst — 2021-03-23T04:02:41+01:00

https://www.cs.ox.ac.uk/people/varun.kanade/teaching/CLT-MT2018/lectures/

C6.5 Theories of Deep Learning (2020-2021) | Mathematical Institute Course Management

analyst — 2021-03-23T03:56:29+01:00

https://courses.maths.ox.ac.uk/node/49612/materials

B3.2 Geometry of Surfaces - Material for the year 2020-2021 | Mathematical Institute Course Management

analyst — 2021-03-23T02:15:58+01:00

https://courses.maths.ox.ac.uk/node/48839

Deep Learning & Reinforcement Learning

analyst — 2021-02-24T19:51:41+01:00

Chris G. Willcocks, Durham University

Data Science: principles and practice – Course materials

analyst — 2021-01-18T18:03:54+01:00

https://www.cl.cam.ac.uk/teaching/2021/DataSciII/materials.html

MIT EECS Education Portal - Fall, 2020

analyst — 2020-10-30T09:03:36+01:00

Courses offered in Fall-2020

Teaching – StachnissLab

analyst — 2020-10-22T08:21:21+02:00

- Modern C++ for Computer Vision - 3D Coordinate Systems - Photogrammetry I - Mobile Sensing and Robotics I - Photogrammetry II - Mobile Sensing and Robotics II - Techniques for Self-Driving Cars - Master Project

Modern C++ Course for CV (2020) – StachnissLab

analyst — 2020-10-22T08:19:36+02:00

https://www.ipb.uni-bonn.de/teaching/cpp-2020/

CS 330: Deep Multi-Task and Meta Learning

analyst — 2020-10-13T13:11:38+02:00

While deep learning has achieved remarkable success in supervised and reinforcement learning problems, such as image classification, speech recognition, and game playing, these models are, to a large degree, specialized for the single task they are trained for. This course will cover the setting where there are multiple tasks to be solved, and study how the structure arising from multiple tasks can be leveraged to learn more efficiently or effectively. This includes: - goal-conditioned reinforcement learning techniques that leverage the structure of the provided goal space to learn many tasks significantly faster - meta-learning methods that aim to learn efficient learning algorithms that can learn new tasks quickly - curriculum and lifelong learning, where the problem requires learning a sequence of tasks, leveraging their shared structure to enable knowledge transfer This is a graduate-level course. By the end of the course, students will be able to understand and implement the state-of-the-art multi-task learning and meta-learning algorithms and be ready to conduct research on these topics.

Theory of Reinforcement Learning | Simons Institute for the Theory of Computing

analyst — 2020-10-12T13:31:38+02:00

This program aims to reunite researchers across disciplines that have played a role in developing the theory of reinforcement learning. It will review past developments and identify promising directions of research, with an emphasis on addressing existing open problems, ranging from the design of efficient, scalable algorithms for exploration to how to control learning and planning. It also aims to deepen the understanding of model-free vs. model-based learning and control, and the design of efficient methods to exploit structure and adapt to easier environments.

Foundations of Deep Learning | Simons Institute for the Theory of Computing

analyst — 2020-10-12T13:29:43+02:00

The program focused on the following four themes: - Optimization: How and why can deep models be fit to observed (training) data? - Generalization: Why do these trained models work well on similar but unobserved (test) data? - Robustness: How can we analyze and improve the performance of these models when applied outside their intended conditions? - Generative methods: How can deep learning be used to model probability distributions?

Groups and Group Actions: Lecture 0 | Theorem of the week

analyst — 2020-10-01T10:32:33+02:00

Welcome to the course blog for the Oxford first year Groups and Group Actions course (Hilary and Trinity Terms 2017). I hope that this will be a useful resource to accompany the lectures, problems sheets and tutorials. Please check back after each lecture for a new post. In addition, I have a course page with some useful information, and…

Geometry Processing and Applications WS19

analyst — 2020-07-01T20:40:16+02:00

https://wordpress.discretization.de/geometryprocessingandapplicationsws19/2019/10/17/lecture-progress/

EE 270 - Large Scale Matrix Computation, Optimization and Learning

analyst — 2020-05-30T21:08:35+02:00

https://web.stanford.edu/class/ee270/