I had the class on Wednesday, to be honest, it made me confused, for I had no idea what I shall do and how. The name of the course is appealing and the central concept is novel from my perspective, thus I determined to be fully engaged in this course and figure out what I might get from it.
At the very beginning of the class, some terms were intricate, as it puzzled me how these distinct subjects or territories got correlated, some having nothing to do with others intuitively. It is the process of coping with the task that really capacitate capacitates me to get gradually understand the essence of the course which is designed to facilitate the formation of learning patterns.
What is the pattern? Of course, it is the methodology of study and can be roughly attributed to three aspects:
== ''' Contrast''' ==
'''Contrast does not mean comparing two irrelevant issues or references, instead it urges us to visualize things through different perceptions and form an interrelated network to reinforce our interpretation of certain concepts and theories etc.'''
'''(1) Persons'''
1. John Von Neumann, Einstein, Newton, Alan Turing, Johannes Kepler.
2. Aristotle, Ptolemy, Newton, Franklin, Lavoisier, Lyell, Planck, Einstein, Hauksbee, Gray, Desaguliers, Du Fray, Nollett, Watson, Boyle, Boerhaave, Hutton, Cavendish, Coulombs and Volta.
'''(2) Technologies'''
1. Computers, Einstein’s theory of general relativity, Newton’s laws, Kepler’s Laws of Planetary Obits, Alan Turing’s calculation, Turing test, Ecorithm and PAC.
2. Aristotle’s Physica, Ptolmey’s Almagest, Newton’s Principia and Opticks, Franklin’s Electricity, Lovoisier’s Chemistry, Lyell’s Geology and quantum mechanism.
'''(3) Content'''
Both books describe the revolution of science. The 1st book refers to the terms of CS while the 2nd book describes it using variable examples and facts. However, both chapters are telling the same main point. The science evolves from experience and facts accumulated and gradually based on systematical learning. Learning is then divided by two categories; one is explanatory learning in the 1st book (normal science or paradigm in the 2nd book), the other being machine learning (paradigm theory predictions and fact gathering). The most significant difference between these two books is their terminology that is restricted by the writers’ research territory. While Leslie Valiant utilizes words like PAC and ecorithm as evolving algorithm, Thoma S. Kuhn stress more on the history and the development of electric and optic physics.