The Learning Curve

Lecture Notes

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  [ML Lecture 01] Introduction to ML 1. Programming in the ModulePythonNumpyPandasScipyMatplotlibKerasPyTorchScikitlearn 2. Machine LearningArtificial Interlligence (AI)Machine Learning (ML) : A subset of AI that enables systems to learn patterns from data, different from traditional solutions relying on explicitly programmed rules. Deep Learning (DL) : A subset of ML based on multi-layerd artificial newral networks, inspired in pa.. 댓글 0 2026.10.13

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  [Lecture 01] Introduction to Data Mining 1. OverviewWhy Do We Need Data Mining? We live in a world where enormous amounts of data are generated every day. However, collecting data does not automatically lead to useful knowledge. The purpose of data mining is to discover meaningful patterns, relationships and insights from data. The growth of data is often described using the 3Vs of Big Data:Volume: The amount of data being generated a.. 댓글 0 2026.10.12

• Python SciPy 베르누이 분포(Bernoulli Distribution)와 주요 Methods 정리 베르누이 분포 (Bernoulli Distribution): 결과가 두 가지뿐인 실험을 나타내는 확률 분포ex) 동전 앞/뒤, 검사 정상/불량P(X = 1) = p P(X = 0) = 1 - p stats.bernoullifrom scipy import stats rvs() - Random Variates: 랜덤 값 생성stats.bernoulli.rvs(0.7)for x in range(0, 5): print(rand_variable_0p3.rvs(4))"""[0 0 0 0][1 0 0 0][1 0 0 0][1 0 0 0][0 0 0 0]""" pmf() - Probabiliey Mass Function: 정확히 이 값이 나올 확률은 얼마인가?stats.bernoulli.pmf(x, p)# Yes.. 댓글 0 2026.09.24

• python programing 수업 노트: strings StringSlice: backward & last stringsword = testword[::-1]word[-2:]Methodsstring.lower() & string.upper()UPPER = text.upper()lower = text.lower()# re-allocationstring.capitalize()Capitalize = text.capitalize()# re-allocationsplit & join# string to listtext_list = string.split()# list to stringnew_string = ' '.join(text_list)replacestring.replace(old, new, count)Package: Counter()from collections .. 댓글 0 2026.09.09

• python programing 수업 노트: functions 1. Review for Loop“When they stop?”break: stops loop immediatelycontinue: skips to the next iteration# break# continue2. FunctionsPackages: math, timeimport math #, time (X), import separatelyimport timeTime: end_time - start_timeimport mathtime_start = time()for i in range(0, 2000000): math.sqrt(i)time_end = time()print('This took', time_end - time_start, 'seconds.')# Output# This took 0.18.. 댓글 0 2026.09.09