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Human Activity Recognition using smartphones
Assignment: Apply Random Forests & GBDT ?
Assignment: Apply Random Forests & GBDT ?
Instructor:
Applied AI Course
Duration:
4 mins
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Kaggle competitions vs Real world
Support Vector Machines (SVM)
1.1
Geometric Intution
20 min
1.2
Mathematical derivation
17 min
1.3
Loss function (Hinge Loss) based interpretation
18 min
1.4
Dual form of SVM formulation
16 min
1.5
Kernel trick
10 min
1.6
Polynomial kernel
11 min
1.7
RBF-Kernel
21 min
1.8
Domain specific Kernels
6 min
1.9
Train and run time complexities
8 min
1.10
nu-SVM: control errors and support vectors
6 min
1.11
SVM Regression
8 min
1.12
Cases
9 min
1.13
Code Sample
14 min
1.14
Exercise: Apply SVM to Amazon reviews dataset
4 min
Decision Trees
2.1
Geometric Intuition: Axis parallel hyperplanes
17 min
2.2
Sample Decision tree
8 min
2.3
Building a decision Tree:Entropy
19 min
2.4
Building a decision Tree:Information Gain
10 min
2.5
Building a decision Tree: Gini Impurity
7 min
2.6
Building a decision Tree: Constructing a DT
21 min
2.7
Building a decision Tree: Splitting numerical features
8 min
2.8
Feature standardization
4 min
2.9
Building a decision Tree:Categorical features with many possible values
7 min
2.10
Overfitting and Underfitting
8 min
2.11
Train and Run time complexity
7 min
2.12
Regression using Decision Trees
9 min
2.13
Cases
12 min
2.14
Code Samples
9 min
2.15
Exercise: Decision Trees on Amazon reviews dataset
3 min
Ensemble Models
3.1
What are ensembles?
6 min
3.2
Bootstrapped Aggregation (Bagging) Intuition
17 min
3.3
Random Forest and their construction
15 min
3.4
Bias-Variance tradeoff
7 min
3.5
Bagging :Train and Run-time Complexity.
9 min
3.6
Bagging:Code Sample
4 min
3.7
Extremely randomized trees
8 min
3.8
Random Tree :Cases
6 min
3.9
Boosting Intuition
17 min
3.10
Residuals, Loss functions and gradients
13 min
3.11
Gradient Boosting
10 min
3.12
Regularization by Shrinkage
8 min
3.13
Train and Run time complexity
6 min
3.14
XGBoost: Boosting + Randomization
14 min
3.15
AdaBoost: geometric intuition
7 min
3.16
Stacking models
22 min
3.17
Cascading classifiers
15 min
3.18
Kaggle competitions vs Real world
9 min
3.19
Assignment: Apply Random Forests & GBDT ?
4 min
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