cross-posted from: https://lemmy.ml/post/14647640

Not sure if this is allowed here, and it's not my playlist, but I thought I'd post these tutorials since I've found them helpful for learning the basics.

• Institution: Socratica
• Subject: #python
• Description (copied from Socratica's YT channel): Our Python Tutorials will help you learn Python quickly and thoroughly. We start with "Hello World" and then move on to data structures (sets, lists, tuples and dictionaries). Next we'll cover classes, and give a variety of in-depth examples and applications.

Institution: UCLA

Lecturer: Professor Courtenay Raia

University Course Code: HIST 2D

Subject: #history #science #religion #magic #antiquity #modernity

Year: 2009

Description: Professor Courtenay Raia lectures on science and religion as historical phenomena that have evolved over time. Examines the earlier mind-set before 1700 when into science fitted elements that came eventually to be seen as magical. The course also question how Western cosmologies became "disenchanted." Magical tradition transformed into modern mysticisms is also examined as well as the political implications of these movements. Includes discussion concerning science in totalitarian settings as well as "big science" during the Cold War.

Cross post of my post from elsewhere. I was told y'all might appreciate this.

Institution: Wikiversity
Lecturer: Boud Roukema
Subject: #physics #specialrelativity #generalrelativity
Description: Special relativity and steps towards general relativity is a one-semester Wikiversity course that uses the geometrical approach to understanding special relativity and presents a few elements towards general relativity. The course may be used in a traditional university, within the conditions of the free licensing terms indicated at the bottom of this Wikiversity web page. It may be modified and redistributed according to the same conditions, for example, via the Wikiversity and Wikimedia Commons web sites.

Do you post your full lectures online? Please consider sharing them to @opencourselectures. A community dedicated to collecting and discussing freely available full length lecture series for autodidacts and personal development!

I believe the distribution of knowledge is by far the most important function of the internet and I need your help to bring some of that functionality to the fediverse.

Please boost for visibility!

@academicchatter

Institution: Stanford
Lecturer: Fei-Fei Li, Justin Johnson, Serena Yeund
University Course Code: CS 231
Subject: #computervision #machinelearning

Description: Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This lecture collection is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. From this lecture collection, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision.

Institution: Yale
Lecturer: Ben Polak
University Course Code: Econ 159
Subject: #econ #economics
Description: This course is an introduction to game theory and strategic thinking. Ideas such as dominance, backward induction, Nash equilibrium, evolutionary stability, commitment, credibility, asymmetric information, adverse selection, and signaling are discussed and applied to games played in class and to examples drawn from economics, politics, the movies, and elsewhere.

Institution: MIT
Lecturer: many
University Course Code: MIT 6.S191
Subject: #machinelearning #ml #deeplearning
Description: MIT's introductory program on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Program concludes with a project proposal competition with feedback from staff and panel of industry sponsors. Prerequisites assume calculus (i.e. taking derivatives) and linear algebra (i.e. matrix multiplication), we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. Listeners are welcome!

Institution: Yale
Lecturer: Ron Smith
University Course Code: GG 140
Subject: #climate #earthscience
Description: This course explores the physical processes that control Earth's atmosphere, ocean, and climate. Quantitative methods for constructing mass and energy budgets. Topics include clouds, rain, severe storms, regional climate, the ozone layer, air pollution, ocean currents and productivity, the seasons, El Niño, the history of Earth's climate, global warming, energy, and water resources.

Institution: DeepMind x UCL
Lecturer: Hado van Hasselt
University Course Code: na
Subject: #machinelearning #ml

Institution: Yale
Lecturer: Paul H. Fry
University Course Code: ENGL 300
Subject: #lit #literature
Description: This is a survey of the main trends in twentieth-century literary theory. Lectures will provide background for the readings and explicate them where appropriate, while attempting to develop a coherent overall context that incorporates philosophical and social perspectives on the recurrent questions: what is literature, how is it produced, how can it be understood, and what is its purpose?

Institution: Knauss School of Business University of San Diego
Lecturer: Fred Freundlich (professor at Mondragon University)
Subject: #modragon #coops
Year: 2014
Description: Mondragon is one of the leading international examples of cooperative ownership of enterprise and inter-firm collaboration. Mondragon has not only made these characteristics its foundational values, but it also uses them strategically, to complete around the world in advanced manufacturing, retail, finance and knowledge. This presentation introduces Mondragon, its special features and its particular international challenges.

Professor Ian Hodder is the Dunlevie Family Professor in the department of anthropology at Stanford University. He also led the Çatalhöyük Research Project, directing an international team of archaeologists in excavating Çatalhöyük, a 9,000-year-old site in Turkey and one of the world’s first urban centers. His work contributes to our understanding of the development of one of the world’s earliest societies, the social and economic organization of the settlement, and the transformation from hunting and gathering to agriculture and civilization.

Institution: MIT
Lecturer: Prof. Norvin A. Richards
University Course Code: MIT 24.900
Subject: #linguistics
Description: This class provides some answers to basic questions about the nature of human language. Throughout the course, we examine a number of ways in which human language is a complex but law-governed mental system. Much of the class is devoted to studying some core aspects of this system in detail; we also spend individual classes discussing a number of other issues, including how language is acquired, how languages change over time, language endangerment, and others.

Just started a new Lemmy community for sharing freely available full-length college courses and lectures. You can follow from Mastodon at @opencourselectures
#lemmy #fediverse #academia #selfstudy #learning

Institution: Yale
Lecturer: John Geanakoplos
University Course Code: ECON 251
Subject: #economics #finance
Description: This course attempts to explain the role and the importance of the financial system in the global economy. Rather than separating off the financial world from the rest of the economy, financial equilibrium is studied as an extension of economic equilibrium. The course also gives a picture of the kind of thinking and analysis done by hedge funds.

Institution: MIT
Lecturer: Dr. Casey Rodriguez
Course Code: MIT 18.100A
Subject: #math
Description: This course covers the fundamentals of mathematical analysis: convergence of sequences and series, continuity, differentiability, Riemann integral, sequences and series of functions, uniformity, and the interchange of limit operations. It shows the utility of abstract concepts through a study of real numbers, and teaches an understanding and construction of proofs.

Institution: MIT
Lecturer: Paige Bright
University Course Code: MIT 18.S190
Subject: #math #metricspaces
Description: How do we go from real analysis on Euclidean space to more general settings? We use metric spaces! In this six-lecture course we develop the general theory of metric spaces, including compact sets, complete metric spaces, and much more.

Institution: MIT
Lecturer: Esther Duflo
University Course Code: MIT 14.771
Subject: #econ
Description: This course provides rigorous introduction to core microeconomic issues in economic development, focusing on both key theoretical contributions and empirical applications to understand both why some countries are poor and on how markets function differently in poor economies. Topics include human capital (education and health); labor markets; credit markets; land markets; firms; and the role of the public sector.

cross-posted from: https://feddit.de/post/4236874

Anyone interested in game design and would like to learn more, I can only recommend this free MIT course.

Institution: Yale.
Lecturer: Robert Shiller.
University Course Code: ECON 252.
Subject: #econ #economics #finance.
Description: An overview of the ideas, methods, and institutions that permit human society to manage risks and foster enterprise. Description of practices today and analysis of prospects for the future. Introduction to risk management and behavioral finance principles to understand the functioning of securities, insurance, and banking industries.

Institution: Cambridge
Lecturer: Petar Velickovic
University Course Code: seminar
Subject: #math #machinelearning #neuralnetworks
Description: Deriving graph neural networks (GNNs) from first principles, motivating their use, and explaining how they have emerged along several related research lines.