CSE427

Machine Learning

[NWT, SWG]49 Videos

Decision trees, ensemble methods, logistic regression, PCA, neural networks, CNN, RNN, LSTM, and transformers.

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49 Videos

Decision & Classification Trees

2
1
Decision Tree (ID3 Algorithm) (Spring 2026) [NWT]

Decision Tree (ID3 Algorithm) (Spring 2026) [NWT]

1:10:38
2
Decision and Classification Trees [StatQuest]

Decision and Classification Trees [StatQuest]

18:08

Regression Tree

2
1
Regression Tree (Spring 2026) [NWT]

Regression Tree (Spring 2026) [NWT]

1:12:27
2
Regression Trees [StatQuest]

Regression Trees [StatQuest]

22:33

Random Forest

3
1
Random Forest and Finding Missing Value (Spring 2026) [NWT]

Random Forest and Finding Missing Value (Spring 2026) [NWT]

1:26:28
2
Random Forests Part 1 - Building, Using and Evaluating [StatQuest]

Random Forests Part 1 - Building, Using and Evaluating [StatQuest]

9:54
3
Random Forests Part 2 - Missing Data and Clustering [StatQuest]

Random Forests Part 2 - Missing Data and Clustering [StatQuest]

11:53

AdaBoost

2
1
AdaBoost [StatQuest]

AdaBoost [StatQuest]

20:54
2
AdaBoost Example Solution

AdaBoost Example Solution

37:30

Naive Bayes

1
1
Naive Bayes [StatQuest]

Naive Bayes [StatQuest]

15:12

Logistic Regression Part I

3
1
Logistic Regression Example and Loss Function Derivation (Fall 2025) [NWT]

Logistic Regression Example and Loss Function Derivation (Fall 2025) [NWT]

1:35:35
2
Logistic Regression Example and Loss Function Derivation (Spring 2026) [NWT]

Logistic Regression Example and Loss Function Derivation (Spring 2026) [NWT]

1:22:15
3
Logistic Regression and Neural Network Perceptrons [SWG]

Logistic Regression and Neural Network Perceptrons [SWG]

2:56:14

Logistic Regression Part II

1
1
Logistic Regression Part II Examples (Spring 2026) [NWT]

Logistic Regression Part II Examples (Spring 2026) [NWT]

1:00:02

Gradient Descent & Chain Rule

2
1
Gradient Descent, Step-by-Step [StatQuest]

Gradient Descent, Step-by-Step [StatQuest]

23:54
2
The Chain Rule [StatQuest]

The Chain Rule [StatQuest]

18:24

Gradient Boosting

4
1
Gradient Boosting Regression Problem [NWT]

Gradient Boosting Regression Problem [NWT]

31:40
2
Gradient Boosting Classification Problem [NWT]

Gradient Boosting Classification Problem [NWT]

29:03
3
Gradient Boost Part 1 - Regression Main Ideas [StatQuest]

Gradient Boost Part 1 - Regression Main Ideas [StatQuest]

15:52
4
Gradient Boost Part 3 - Classification [StatQuest]

Gradient Boost Part 3 - Classification [StatQuest]

17:03

Performance Evaluation & ROC-AUC

4
1
Performance Evaluation and ROC-AUC [NWT]

Performance Evaluation and ROC-AUC [NWT]

1:15:29
2
The Confusion Matrix [StatQuest]

The Confusion Matrix [StatQuest]

7:13
3
Sensitivity and Specificity [StatQuest]

Sensitivity and Specificity [StatQuest]

11:47
4
ROC and AUC [StatQuest]

ROC and AUC [StatQuest]

16:17

PCA

2
1
PCA Concepts [NWT]

PCA Concepts [NWT]

1:06:12
2
PCA Math [NWT]

PCA Math [NWT]

42:47

Neural Network

1
1
The Essential Main Ideas of Neural Networks [StatQuest]

The Essential Main Ideas of Neural Networks [StatQuest]

18:54

Parameter Update & Backpropagation

7
1
Neural Network Parameter Update with ReLU Function Full (Spring 2026) [NWT]

Neural Network Parameter Update with ReLU Function Full (Spring 2026) [NWT]

2:21:10
2
Neural Network Parameter Update [NWT]

Neural Network Parameter Update [NWT]

1:05:38
3
Neural Networks Pt. 2 - Backpropagation Main Ideas [StatQuest]

Neural Networks Pt. 2 - Backpropagation Main Ideas [StatQuest]

17:34
4
Backpropagation Details Pt. 1 - Optimizing 3 Parameters Simultaneously [StatQuest]

Backpropagation Details Pt. 1 - Optimizing 3 Parameters Simultaneously [StatQuest]

18:32
5
Backpropagation Details Pt. 2 - Going Bonkers with the Chain Rule [StatQuest]

Backpropagation Details Pt. 2 - Going Bonkers with the Chain Rule [StatQuest]

13:09
6
Neural Networks Pt. 3 - ReLU in Action [StatQuest]

Neural Networks Pt. 3 - ReLU in Action [StatQuest]

8:58
7
Neural Networks Part 5 - ArgMax and SoftMax [StatQuest]

Neural Networks Part 5 - ArgMax and SoftMax [StatQuest]

14:03

CNN

6
1
CNN Part 1 (Spring 2026) [NWT]

CNN Part 1 (Spring 2026) [NWT]

1:52:45
2
CNN - Kernel, Stride, Padding, Pooling, Flatten, Formula [Binod Suman Academy]

CNN - Kernel, Stride, Padding, Pooling, Flatten, Formula [Binod Suman Academy]

21:32
3
What is Convolution Operation in CNN? [Krish Naik]

What is Convolution Operation in CNN? [Krish Naik]

10:58
4
Padding in Convolutional Neural Network [Krish Naik]

Padding in Convolutional Neural Network [Krish Naik]

7:50
5
Operation of CNN (CNN vs ANN) [Krish Naik]

Operation of CNN (CNN vs ANN) [Krish Naik]

11:10
6
Max Pooling Layer in CNN [Krish Naik]

Max Pooling Layer in CNN [Krish Naik]

6:37

RNN

2
1
RNN and LSTM (Spring 2026) [NWT]

RNN and LSTM (Spring 2026) [NWT]

1:32:47
2
Recurrent Neural Networks (RNNs) [StatQuest]

Recurrent Neural Networks (RNNs) [StatQuest]

16:37

LSTM

2
1
LSTM [NWT]

LSTM [NWT]

1:08:45
2
Long Short-Term Memory (LSTM) [StatQuest]

Long Short-Term Memory (LSTM) [StatQuest]

20:45

Word Embedding

1
1
Word Embedding and Word2Vec [StatQuest]

Word Embedding and Word2Vec [StatQuest]

16:12

Transformer

4
1
Transformer Full [NWT]

Transformer Full [NWT]

1:21:21
2
Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks [StatQuest]

Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks [StatQuest]

16:50
3
Attention for Neural Networks [StatQuest]

Attention for Neural Networks [StatQuest]

15:51
4
Transformer Neural Networks [StatQuest]

Transformer Neural Networks [StatQuest]

36:15