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Important questions
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UNIT I
1.ML Apps, (VC) dimension,
2.(PAC) learning
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UNIT II
1.Least squares, single & multiple variables, Bayesian linear regression ***
2.Naive Bayes, Maximum margin classifier Support 3.vector machine, Decision Tree**
UNIT III
1. Gaussian mixture models and Expectation maximization ***
2.bagging,boosting, stacking,(k means **).
UNIT IV
1.Activation functions, network training gradient descent optimization
2.Unit saturation (aka the vanishing gradient problem) ReLU***
3.hyperparameter tuning. batch normalization, regularization**
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UNIT V
1. Rare :- (CV) and resampling
2. measuring classifier performance assessing a single classification algorithm and comparing two classification algorithms ****
3.t test. McNemar's test, K-fold CV paired t test.**may be part c
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**Very important questions are bolded and may be asked based on this topic
PART-C
1.Compulsory Questions {a case study where the student will have to read and analyse the subject }
mostly asked from unit 2, 5(OR) a situation given and you have to answer on your own
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*These questions are expected for the exams This may or may not be asked for exams All the best.... from admin Santhosh
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UNIT I INTRODUCTION TO MACHINE LEARNING
Review of Linear Algebra for machine learning Introduction and motivation for machine learning: Examples of machine learning applications, Vapnik-Chervonenkis (VC) dimension, Probably Approximately Correct (PAC) learning, Hypothesis spaces, Inductive bias, Generalization Bias variance trade-off.
UNIT II SUPERVISED LEARNING
Linear Regression Models: Least squares, single & multiple variables, Bayesian linear regression, gradient descent, Linear Classification Models Discriminant function Perceptron algorithm, Probabilistic discriminative model Logistic regression, Probabilistic generative model Naive Bayes, Maximum margin classifier Support vector machine, Decision Tree, Random Forests
UNIT III ENSEMBLE TECHNIQUES AND UNSUPERVISED LEARNING
Combining multiple learners: Model combination schemes, Voting, Ensemble Learning
bagging, boosting, stacking Unsupervised learning: K-means, Instance Based Learning: KNN, Gaussian mixture models and Expectation maximization.
UNIT IV NEURAL NETWORKS
Multilayer perceptron, activation functions, network training gradient descent optimization stochastic gradient descent, error backpropagation, from shallow networks to deep networks Unit saturation (aka the vanishing gradient problem) - ReLU, hyperparameter tuning, batch normalization, regularization, dropout.
UNIT V DESIGN AND ANALYSIS OF MACHINE LEARNING EXPERIMENTS
Guidelines for machine learning experiments, Cross Validation (CV) and resampling - K-fold CV. bootstrapping, measuring classifier performance assessing a single classification algorithm and comparing two classification algorithms - t test. McNemar's test, K-fold CV paired t test.