Machine Learning (B20AM3103)

Syllabus

Question Bank and Objective Type Questions

Unit - 1

Part -1 (The Ingredients of Machine Learning)

1. Introduction :  [Lecture_Notes] [PPT] 

2. Introduction Part 2 : [Lecture_Notes] [PPT]

3. Tasks and their Classification:  [Lecture_Notes] [PPT] 

4. Models, Geometric Models:  [Lecture_Notes] [PPT] 

5. Kmeans Clustering, Probabilistic Models :  [Lecture_Notes] [PPT] 

6. Logical Models, Grouping & Grading Models :  [Lecture_Notes] [PPT] 

7. Features, Machine Learning Process :  [Lecture_Notes] [PPT] 

Part -2 (Preliminaries)

1. The curse of dimensionality   [Lecture_Notes ] [PPT]

2. Training, Test and Validation sets, The confusion matrix, The accuracy metrics: Accuracy, sensitivity, specificity, precision, recall, F1 measure [Lecture Notes] [PPT]

3. Prior Probability, Conditional Probability, Naïve Bayesian Classifier          [Lecture_Notes] [PPT]

4. Naïve Bayesian Classifier Example [Lecture_Notes] [PPT]

5. Some Basic Statistics: Variance, Covariance, Bias-Variance Tradeoff , Overfitting  , Underfitting,  ROC Curve,  Unbalanced Datasets [Lecture_Notes] [PPT]

Unit - 2

1. Tree Models : Decision Trees [Lecture_Notes][PPT] 

2. Distance Based Classification: Nearest Neighbour Classification [Lecture_Notes][PPT] [KNN_Example]

3. Linear Models

• Univariate Linear Regression [Lecture_Notes] [Method_of_Least_Squares] [PPT 1] [PPT 2] 

• Logistic Regression [PPT]

• Support Vector Machines 

  • Part 1 [PPT] 

  • Part 2: Extra Slides from Another Professor[PPT] 

Unit - 3

1. Kinds of feature, Feature transformations: Thresholding and discretization, Normalization, Incomplete Features, Feature construction [PPT]

2. Feature Selection [PPT]

3. Bagging, Random Forests, Boosting: AdaBoost [PPT]

4. Gradient Boosting [Lecture_Notes] [PPT1] [PPT2 (Optional] [Algorithm][Video_Lecture1] [Video_Lecture2]

5. XGBoost [PPT] [Video_Lecture]

6. Voting Classifier [PPT]

Unit - 4

1. PCA [PPT]

2. LDA [PPT]

3. Cross Validation and Grid Search [PPT]

4. Regularization [PPT]

Unit -5 

1. Neurons, NNs, Linear Discriminants: The Neuron, Neural Networks, The perceptron [PPT]

2. Going forwards, Going backwards: Backpropagation of error, Multilayer perceptron in practice, Examples of using MLP [PPT1][PPT2]

3. Reinforcement Learning [PPT]