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: 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: 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: 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: 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-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: Yale

Lecturer: Professor Shelly Kagan

University Course Code: PHIL 176

Subject: #philosophy #death #metaphysics #valuetheory

Year: Spring 2007

Description: There is one thing I can be sure of: I am going to die. But what am I to make of that fact? This course will examine a number of issues that arise once we begin to reflect on our mortality. The possibility that death may not actually be the end is considered. Are we, in some sense, immortal? Would immortality be desirable? Also a clearer notion of what it is to die is examined. What does it mean to say that a person has died? What kind of fact is that? And, finally, different attitudes to death are evaluated. Is death an evil? How? Why? Is suicide morally permissible? Is it rational? How should the knowledge that I am going to die affect the way I live my life?

Course materials can be found on the Open Yale Courses website.

Institution: MIT

Lecturer: Julian Beinart

University Course Code: 4.241J

Subject: #architecture #urbanstudies #finearts #socialscience

Year: Spring 2013

Description: This course covers theories about the form that settlements should take and attempts a distinction between descriptive and normative theory by examining examples of various theories of city form over time. Case studies will highlight the origins of the modern city and theories about its emerging form, including the transformation of the nineteenth-century city and its organization. Through examples and historical context, current issues of city form in relation to city-making, social structure, and physical design will also be discussed and analyzed.

Course materials can be found on the MIT OpenCourseWare website.

An in depth introduction to building your own computer from individual chips to help form a solid foundation of how computers work.