Explore the basic principles of machine learning, focusing on regression as a simple example of gathering data, identifying causal relationships, and making predictions about the future.
- [Voiceover] When we think of machine learning, … perhaps we visualize self-driving cars … or humanoid robots. … Or maybe we think of some … of the high profile achievements … in machine learning systems. … Competing with humans like Watson, winning at Jeopardy! … Or AlphaGo beating the human Go champion Lee Sedol. … These are the most technically sophisticated … and computationally powerful examples of machine learning. … Systems that took millions of dollars … to design, train, and test. … They are useful examples of the fundamental aspiration … of machine learning to build generalized decision making … and problem solving systems. … Understand the principles and techniques underlying … the pursuit of this aspiration may be more useful … to consider machine learning at its simplest. … Imagine I throw a ball in the air. … In a second it comes back down on my head. … Now I throw it 100 more times. … Each time the ball lands on my head. … Now, with a mild headache, I throw the ball … in the air and raise my hands …
- What is generative design?
- Limitations of generative design
- Strengths and limitations of genetic/evolutional solvers
- How physics solvers work
- Testing and adjusting goals
- Working with machine learning tools
- Design requirements and diagramming
- Optimizing with Galapagos
Skill Level Intermediate
Grasshopper: Architectural Prototypingwith Walter Zesk2h 28m Intermediate
Learning Algorithmic Design with Grasshopperwith Chris Reilly3h 14m Intermediate
1. What Is Generative Design?
2. Genetic/Evolutional Solver Example
3. Physics Solver Example
4. Machine Learning Solver Example
5. Applying Generative Design
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