LECTURES

Machine Learning Tutorial Seminar

Video Lectures (by Prof. Sang-Jo Yoo) Table of Contents Lecture List

1:Introduction

2:LinearRegression

3:TesorFlowBasics

4:TensorManipulation

5:Code(Regression)

6:DecisionTree

7:RandomForest&AdaBoost

8:Clustering(1)

9:Clustering(2)

10:Clustering(3)

11:BayesClassifer

12:SVM(1)

13:SVM(2)

14:SigmoidSoftmax

15:Code(SigmoidSoftmax)

16:BackPropagation

17:Normalization

18:Code(Normalization)

19:Optimizer

20:Regularization&Dropout

21:DNNinitilization

22:Code(DNNmnist)

23:CNN

24:Code(CNN)

25:GAN

26:Code(GAN)

27:RNN(1)

28:RNN(2)

29:Code(RNN)

30:ReinforcementLearning(1)

31:ReinforcementLearning(2)

32:RL(Q-learning)

33:RL(DQN)

34:Code(Q-learning)

Lecture Note Download

Reinforcement Learning Tutorial Seminar

Video Lectures (by Prof. Sang-Jo Yoo) Table of Contents:

Lecture Note Download

Undergraduate School

Probability and Random Variables
Signal and Systems
Computer Networks
Internet Protocols and Programming
Other Previous Lectures: Digital Communications, Digital Signal Processing, Visual C++, Object-oriented Programming

Graduate School

Reinforcement Learning
Wireless Sensor Networks
Cognitive Radio Systems
Other Previous Lectures: Mobile Networks, Multimedia Communications, Advanced Computer Networks