ARTIFICIAL INTELLIGENCE

Note: Lecture slides are best viewed in Chrome.

 

Machine Learning, 2019

Dates Topics with Python Slides Videos Homework Solution
02/19/19 Introduction pdf#01
02/21/19
02/26/19
Linear Algebra iNote#02 pdf#02
pdf#03
pdf#04
youtube#02
02/28/19 Python python_installation
Basic Python
youtube#01
03/05/19 Optimization iNote#03 pdf#05 youtube#02
03/07/19
03/12/19
03/14/19
03/19/19
Regression: Basics
Regression: Overfitting and Regularization
Regression: Examples
iNote#04
iNote#05
iNote#06
pdf#06
pdf#07
pdf#08
youtube#03
03/21/19
03/26/19
03/28/19
04/02/19
Classification: Perceptron
Classification: SVM
Classification: Logistic Regression
iNote#07
iNote#08
iNote#09
pdf#09
pdf#10
pdf#11
youtube#04
04/08/19 Midterm
04/16/19
04/18/19
Classification: K-NN
Classification: Decision Tree
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iNote#11
pdf#12
pdf#13
youtube#04
04/16/19
04/23/19
04/24/19
04/30/19
05/02/19
Clustering: K-means
Statistics
Monte Carlo Simulation
Dim. Reduction: PCA
Dim. Reduction: FDA
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iNote#13
iNote#14
iNote#15
iNote#16
pdf#14
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youtube#05
05/02/19 Independent Component Analysis (ICA) iNote#17
05/07/19 SVD iNote#18 pdf#19
05/09/19 Network (or Graph) Theory iNote#19
05/14/19 From Perceptron to MLP iNote#20 pdf#20
05/16/19
05/21/19
Artifical Neural Networks iNote#21
iNote#22
pdf#21
pdf#22
05/23/19 Dim. Reduction: Autoencoder iNote#23 pdf#23
06/04/19 Final Exam

 

Deep Learning, 2019

Dates Topics with Python Slides Videos Homework Solution
02/19/19 Introduction pdf#01 youtube
02/21/19 Optimization and TensorFlow iNote#01 pdf#02 youtube
youtube
youtube
youtube
02/26/19 Machine Learning: Regression and Perceptron iNote#02 pdf#03 youtube
youtube
youtube
youtube
02/28/19 Python python_installation
Basic Python
03/05/19 Machine Learning: Logistic Regression iNote#02 pdf#03 youtube
youtube
03/07/19 Machine Learning with TensorFlow iNote#03 pdf#04 youtube
youtube
youtube
03/12/19 Optimization: SGD
Optimization: Overfitting
iNote#04
iNote#05
pdf#05
pdf#06
youtube
youtube
youtube
youtube
youtube
03/14/19 From Perceptron to MLP (ANN) iNote#06 pdf#07 youtube
youtube
youtube
youtube
03/19/19
03/26/19
ANN training
ANN and Regularization
iNote#07
iNote#08
pdf#08 youtube
>youtube
>youtube
03/21/19 Artificial Neural Networks (ANN) with MNIST iNote#09 pdf#09
pdf#10
youtube
youtube
youtube
03/26/19
03/28/19
Autoencoder (AE)
Can AE conduct FFT?
Denoising AE (DAE)
iNote#10
iNote#11
iNote#12
pdf#11
pdf#12
pdf#13
youtube
04/09/19 Midterm
04/02/19 Colab Installation iNote#13
04/02/19
04/16/19
Convolution
Convolutional Neural Networks (CNN)
iNote#14
iNote#15
pdf#14
pdf#15
04/18/19 Global Average Pooling (GAP) iNote#16 pdf#16
04/23/19 Modern CNNs
Residual Networks
iNote#17
iNote#18
pdf#17
pdf#18

04/23/19 Convolutional Autoencoders (CAE) iNote#19 pdf#19
04/25/19 Fully Convolutional Networks (FCN) iNote#20 pdf#20
04/25/19 Transfer Learning iNote#21 pdf#21
04/30/19 Style Transfer iNote#22 pdf#22
05/02/19
05/07/19
Generative Adversarial Networks (GAN)
Conditional GAN
iNote#23
iNote#24
pdf#23
05/14/19 Time Series Data
Markov Chain
Hidden Markov Model (HMM)
Kalman Filter
iNote#25
iNote#26
iNote#27
iNote#28
pdf#24
pdf#25
05/21/19 Recurrent Neural Networks (RNN) iNote#29 pdf#26
05/23/19 Dynamic Programming
Markov Decision Process (MDP)
iNote#30
iNote#31
pdf#27
pdf#28
05/28/19 Reinforcement Learning and Q-Learning iNote#32
05/30/19 DQN iNote#33
06/05/19 Final Exam