Training and test error rates

Notice that the training and test error rates of the model are large when the size of the tree is very small. This situation is known as model underfitting. Underfitting occurs because the model has yet to learn the true structure of the data. As a result, it performs poorly on both the training and the test sets. As the number of nodes in the decision tree increases, the tree will have fewer training and test errors. However, once the tree becomes too large, its test error rate begins to increase even though its training error rate continues to decrease. This phenomenon is known as model overfitting. 

The Sub-problems of Pattern Classification

1. Feature Extraction
2. Noise
3. Overfitting
4. Model Selection
5. Prior Knowledge
6. Missing Features
7. Mereology
8. Segmentation
9. Context
10. Invariances
11. Evidence Pooling
12. Costs and Risks
13. Computational Complexity

Business process lifecycle

Pragmatic Thinking and Learning - 48 Tips

1. Always consider the context.
2. Use rules for novices, intuition for experts.
3. Know what you don’t know.
4. Learn by watching and imitating.
5. Keep practicing in order to remain expert.
6. Avoid formal methods if you need creativity, intuition, or inventiveness.
7. Learn the skill of learning.
8. Capture all ideas to get more of them.
9. Learn by synthesis as well as by analysis.
10. Strive for good design; it really works better.
11. Rewire your brain with belief and constant practice.
12. Add sensory experience to engage more of your brain.
13. Lead with; follow with.
14. Use metaphor as the meeting place betweenand.
15. Cultivate humor to build stronger metaphors.
16. Step away from the keyboard to solve hard problems.
17. Change your viewpoint to solve the problem.
18. Watch the outliers: “rarely” doesn’t mean “never.”
19. Be comfortable with uncertainty.
20. Trust ink over memory; every mental read is a write.
21. Hedge your bets with diversity.
22. Allow for different bugs in different people.
23. Act like you’ve evolved: breathe, don’t hiss.
24. Trust intuition, but verify.
25. Create SMART objectives to reach your goals.
26. Plan your investment in learning deliberately.
27. Discover how you learn best.
28. Form study groups to learn and teach.
29. Read deliberately.
30. Take notes with bothand.
31. Write on: documenting is more important than documen- tation.
32. See it. Do it. Teach it.
33. Play more in order to learn more.
34. Learn from similarities; unlearn from differences.
35. Explore, invent, and apply in your environment—safely.
36. See without judging and then act.
37. Give yourself permission to fail; it’s the path to success.
38. Groove your mind for success.
39. Learn to pay attention.
40. Make thinking time.
41. Use a wiki to manage information and knowledge.
42. Establish rules of engagement to manage interruptions.
43. Send less email, and you’ll receive less email.
44. Choose your own tempo for an email conversation.
45. Mask interrupts to maintain focus.
46. Use multiple monitors to avoid context switching.
47. Optimize your personal workflow to maximize context.
48. Grab the wheel. You can’t steer on autopilot.

Project knowledge over time

This is your brain

Skill distribution

Dreyfus model of skill acquisition

Decisão

Decidir implica optar por uma alternativa de ação em detrimento de outras disponíveis, em função de preferências, disponibilidades, grau de aceitação do risco etc. Nessa visão, decidir antecipadamente constitui-se em controlar o seu próprio futuro. Essa é uma visão bastante proativa no que se refere ao processo de gestão de certa organização. (ANSOFF, 1977, p.4).

Urban computing

Urban computing is a process of acquisition, integration, and analysis of big and hetero- geneous data generated by diverse sources in urban spaces, such as sensors, devices, ve- hicles, buildings, and humans, to tackle the major issues that cities face (e.g., air pollu- tion, increased energy consumption, and traffic congestion).

I have stood on the shoulders of giants

"Indeed, one of my major complaints about the computer field is that whereas Newton could say, "If I have seen a little farther than others, it is because I have stood on the shoulders of giants," I am forced to say, "Today we stand on each other's feet." Perhaps the central problem we face in all of computer science is how we are to get to the situation where we build on top of the work of others rather than redoing so much of it in a trivially different way. Science is supposed to be cumulative, not almost endless duplication of the same kind of things".

Richard Hamming 1968 Turning Award Lecture 

The Decision Hierarchy

Decision Essentials

Normas da ABNT

Estrutura de uma Monografia

Estilo e Redação de um Texto, Observação e Linguagem Científica

Estrutura de um Projeto e Técnicas de Leitura

Métodos Quantitativos, Qualitativos e Coleta de Dados

Pesquisa - Tema, Problema, Hipótesese Variáveis

O Método Científico e os Tipos de Pesquisa