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회로이론2 (Circuit Theory)

hwjeong — Mon, 13 Jun 2022 08:06:52 +0000

Reference textbook은Introduction to Electric Circuits (9th or 10th Edition) by R. Dorf and J. Svoboda입니다

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강의 신청 – 우측 (PC) 혹은 하단 (Mobile) start now 클릭 후Curriculum을 클릭해주세요

hwjeong — Sat, 21 Aug 2021 01:38:38 +0000

Reference textbook은 Digital Design (Paperback/6th Global Edition) by M. Morris R. Mano, Michael D. Ciletti 입니다

강의자료와 강의영상은 Curriculum을 클릭해주세요

hwjeong — Sun, 11 Jul 2021 05:10:09 +0000

Introduction to Machine Learning (ML)
1-1. Intro to ML (1)
1-2. Intro to ML (2); What will we learn in this course
Probability and Bayesian Learning – ML 공부를 위한 간단한 확률 개념
2-1. Random variable, Conditional probability, and Bayes Rule
2-2. Posterior, Likelihood and Prior
2-3. MAP and MLE; Number game case study
Linear Regression
3-1. Linear regression basic
3-2. Linear regression w/ Probability density
3-3. Python practice for linear regression
Logistic Regression
4-1. Logistic regression basic & python practice
4-2. Application for higher dimension and multiple class
k-NN Classification & Python practice
Clustering
6-1. k-Means & EM algorithm
6-2. Gaussian mixture model (1)
6-3. Gaussian mixture model (2)
6-4. Python practice for k-Means
6-4. Python practice for GMM Clustering
Support vector machine (SVM)
7-1. Intro to SVM
7-2. Intro to Kernel trick
7-3. Lagrange Multiplier and KKT condition for SVM
7-4. SVM development (1) -본격 SVM 배우기
7-5. SVM development (2) -본격 SVM 배우기
7-6. Soft margin and kernel trick for SVM
7-7. Python practice for SVM
Artificial Neural Network; Deep learning basic
8-1. Perceptron
8-2. Multi-layer perceptron (MLP)
8-3. Training for MLP
8-4. Python practice for MLP
Convolution Neural Network (CNN)
9-1. Basic for CNN
9-2. CNN case; Le-Net
9-3. Various CNN structures

hwjeong — Sun, 03 Jan 2021 01:35:24 +0000

강의자료와 강의영상은 Curriculum 탭 참고부탁드립니다.

Reference textbook은 Microelectronics by Behzad Razavi, 2nd edition 입니다.

hwjeong — Thu, 31 Dec 2020 04:29:15 +0000