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공학 >컴퓨터ㆍ통신 >정보과학
강의학기

2023년 2학기

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강의계획서: 강의계획서

AI 의 역사와 머신러닝의 여러 개념들을 소개하고 딥러닝의 기초적인 부분까지 포함한다.

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Course Introduction

차시별 강의

1.	Course Introduction	강의 소개
	Artificial Intelligence. Part1	AI 개요
	Artificial Intelligence. Part2	history and issues
	Machine learning. Part1	Introduction to Machine Learning
	Machine learning. Part2	ML components, data and approaches
	Machine learning. Part3	Applications and prerequisite
2.	Linear Algebra Review. Part1	Calculus for ML
	Linear Algebra Review. Part2	Linear Algebra: Vector
	Linear Algebra Review. Part3	Linear Algebra: Matrix
	Linear Algebra Review. Part4	Linear Algebra: Decompsition and Derivatives
3.	Probability Review. Part1	Probability and statistics
	Probability Review. Part2	Bayes Theorem
	Probability Review. Part3	Gaussian and other distributions
4.	Information Theory. Part1	Information theory, entropy
	Information Theory. Part2	KL, Mutual Information, cross entropy
5.	Density estimation. Part1	density estimation, parametric method
	Density estimation. Part2	non-parametric method and semi-parametric method
6.	Decision Theory. Part1	classification 관련 decision theory
	Decision Theory. Part2	classification 관련 decision theory
	Decision Theory. Part3	classification 관련 decision theory
7.	Clustering. Part1	introduction to clustering
	Clustering. Part2	kMeans Algorithm
	Clustering. Part3	mixture of Gaussian
	Clustering. Part4	EM algorithm
	Clustering. Part5	EM application
8.	Dimension reduction. Part1	차원 축소를 위한 선형 알고리즘
	Dimension reduction. Part2	차원 축소를 위한 선형 알고리즘
	Dimension reduction. Part3	차원 축소를 위한 선형 알고리즘
	Dimension reduction. Part4	차원 축소를 위한 선형 알고리즘
	Dimension reduction. Part5	차원 축소를 위한 선형 알고리즘
9.	Nonlinear Dimension Reduction. Part1	kernel machines and manifold learning
	Nonlinear Dimension Reduction. Part2	kernel machines and manifold learning
	Nonlinear Dimension Reduction. Part3	kernel machines and manifold learning
	Nonlinear Dimension Reduction. Part4	kernel machines and manifold learning
10.	Classification. Part1	분류 기초 및 관련 알고리즘
	Classification. Part2	k nearest neighbor
	Classification. Part3	Na�ve Bayes Classifier
	Classification. Part4	Decision tree part1
	Classification. Part5	Decision tree part2
11.	Ensemble Learning. Part1	ensemble learning 기초
	Ensemble Learning. Part2	why and how it works
12.	Regression. Part1	회귀분석 모델
	Regression. Part2	회귀분석 모델
	Regression. Part3	회귀분석 모델
	Regression. Part4	회귀분석 모델
13.	Neural Networks. Part1	신경망 소개와 역사
	Neural Networks. Part2	forward propagation
	Neural Networks. Part3	backward propagation
	Neural Networks. Part4	training and properties
14.	Optimization. Part1	최적화(gradient descent, Networn method, Trust-region)
	Optimization. Part2	최적화(gradient descent, Networn method, Trust-region)
	Optimization. Part3	최적화(gradient descent, Networn method, Trust-region)
	Optimization. Part4	최적화(gradient descent, Networn method, Trust-region)
	Optimization. Part5	최적화(gradient descent, Networn method, Trust-region)
	Optimization. Part6	최적화(gradient descent, Networn method, Trust-region)
	Optimization. Part7	최적화(gradient descent, Networn method, Trust-region)
15.	Regularization. Part1	overfitting 과 regularization 이해
	Regularization. Part2	overfitting 과 regularization 이해
	Deep Learning. Part1	딥러닝 소개
	Deep Learning. Part2	neuroscientific support and early DL
	Deep Learning. Part3	DL algorithms
	Recommendation. Part1	추천 알고리즘 소개
	Recommendation. Part2	추천 알고리즘 소개
	Recommendation. Part3	추천 알고리즘 소개
	SVM. Part1	SVM 설명
	SVM. Part2	SVM 설명
	SVM. Part3	SVM 설명
	HMM. Part1	HMM 설명
	HMM. Part2	3 problems in HMM