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Deep Learning (딥러닝)


POSTECH
Enrollment in this course is by invitation only

강좌 소개

최근 인공지능 분야에서 주목받고 있는 딥러닝을 이해하고 구현한다.

 This course is designed to exploit and understand Artificial Intelligence, especially Deep Learning. Students are expected to learn theoretical backgrounds and their implementations of algorithms in Python. Starting from a basic machine learning, various kinds of neural networks will be intensively studied. Numerical Python coding is heavily required during lectures and homework assignments.


평가 방법

  • 퀴즈: 20%
  • 과제: 30% 
  • 시험 성적: 50%
    •  - 프로그래밍 시험 (25%)
    •  - 필기 시험 (25%) 


강좌 목차

주차내용
1Introduction & Optimization

Introduction
Optimization 1
Optimization 2
Optimization 3
Lab
2Machine LearningRegression
Classification: Perceptron 1
Classification: Perceptron 2
Classification: Logistic Regression 1
Classification: Logistic Regression 2
Lab
3
Machine Learning with TensorflowMachine Learning with Tensorflow 1
Machine Learning with Tensorflow 2
Regression & Classification with Tensorflow
Lab
4Machine Learning OptimizationStochastic Gradient Descent 1
Stochastic Gradient Descent 2
Lab
Overfitting
Lab
5From Perceptron to MLP (ANN)Artificial Neural Networks 1
Artificial Neural Networks 2
Artificial Neural Networks 3
Lab
Artificial Neural Networks Training 1
Artificial Neural Networks Training 2
Lab
6ANN advancedANN with Tensorflow 1
ANN with Tensorflow 2
Lab
ANN advanced 1
ANN advanced 2
Lab
7Autoencoder (AE) & Convolutional Neural Networks (CNN)

Autoencoder 1
Autoencoder 2
Lab
Convolution: 1D
Convolution: 2D
Convolution: Kernel 1
Convolution: Kernel 2
8Convolutional Neural Networks (CNN) & Class Activation Map (CAM)Convolution: Padding and Stride
Convolution: Pooling
Convolutional Neural Network in Tensorflow
Lab
Class Activation Map (CAM) 1
Class Activation Map (CAM) 2
Lab
9

Modern CNNs & Transfer Learning

Modern CNNs
Lab
Transfer Learning
Lab
10Convolutional Autoencoders (CAE) & Fully Convolutional Networks (FCN)

Convolutional Autoencoders (CAE) 1
Convolutional Autoencoders (CAE) 2
Lab
Fully Convolutional Networks (FCN)
Lab
11Generative Adversarial Networks (GAN)
Generative Adversarial Networks (GAN) 1
Generative Adversarial Networks (GAN) 2
Generative Adversarial Networks (GAN) 3
Generative Adversarial Networks (GAN) 4
12Conditional GAN

Conditional GAN
Lab
13

Time Series Analysis

Time Series Data 1
Time Series Data 2
Markov Chain
Hidden Markov Model (HMM) and Kalman Filter
Lab
14

Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNN) 1
Recurrent Neural Networks (RNN) 2
Recurrent Neural Networks (RNN) 3
Recurrent Neural Networks (RNN) 4
Recurrent Neural Networks (RNN) 5
Recurrent Neural Networks (RNN) 6
Recurrent Neural Networks (RNN) 7
Lab
15Final examsFinal exams


Instructor

이승철

포스텍 기계공학과 교수

UNIST 기계항공및원자력공학부 교수

UNIV. OF MICHIGAN DEPT. OF MECHANICAL ENGINEERING 박사 후 연구원

UNIV. OF MICHIGAN Mechanical Engineering 박사