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AD-502 · Machine Learning/Official Syllabus

Machine Learning (AD-502) - Official Syllabus

1
Unit 1

Introduction to machine learning

  • Introduction to machine learning
  • Machine learning life cycle
  • Types of Machine Learning System (supervised and unsupervised learning, Batch and online learning, Instance-Based and Model based Learning)
  • scope and limitations
  • Challenges of Machine learning
  • data visualization
  • hypothesis function and testing
  • data pre-processing
  • data augmentation
  • normalizing data sets
  • Bias-Variance tradeoff
  • Relation between AI (Artificial Intelligence), ML (Machine Learning), DL (Deep Learning) and DS (Data Science)
2
Unit 2

Clustering in Machine Learning

  • Types of Clustering Method: Partitioning Clustering, Distribution Model-Based Clustering, Hierarchical Clustering, Fuzzy Clustering
  • Birch Algorithm
  • CURE Algorithm
  • Gaussian Mixture Models and Expectation Maximization
  • Parameters estimations – MLE, MAP
  • Applications of Clustering
3
Unit 3

Classification algorithm

  • Logistic Regression
  • Decision Tree Classification
  • Neural Network
  • K-Nearest Neighbors (K-NN)
  • Support Vector Machine
  • Naive Bayes (Gaussian, Multinomial, Bernoulli)
  • Performance Measures: Confusion Matrix, Classification Accuracy, Classification Report: Precisions, Recall, F1 score and Support
4
Unit 4

Ensemble Learning and Random Forest

  • Introduction to Ensemble Learning
  • Basic Ensemble Techniques (Max Voting, Averaging, Weighted Average)
  • Voting Classifiers
  • Bagging and Pasting
  • Out-of-Bag Evaluation
  • Random Patches and Random Subspaces
  • Random Forests (Extra-Trees, Feature Importance)
  • Boosting (AdaBoost, Gradient Boosting)
  • Stacking
5
Unit 5

Dimensionality Reduction

  • The Curse of Dimensionality
  • Main Approaches for Dimensionality Reduction (Projection, Manifold Learning)
  • PCA: Preserving the Variance, Principal Components, Projecting Down to d Dimensions, Explained Variance Ratio, Choosing the Right Number of Dimensions, PCA for Compression, Randomized PCA, Incremental PCA
  • Kernel PCA: Selecting a Kernel and Tuning Hyper parameters
  • Learning Theory: PAC and VC model

Reference Books

  • Machine Learning — Tom M. Mitchell — McGraw Hill Education — First edition
  • Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems — Aurelien Geon — Shroff/O'Reilly — First edition
  • Introduction to Machine Learning with Python: A Guide for Data Scientists — Andreas Muller — Shroff/O'Reilly — First edition
  • Finding groups in data: An introduction to cluster analysis — Leonard Kaufman, P. J. Rousseau — Wiley
  • An Introduction to Support Vector Machines — Nello Cristianini, John Shawe-Taylor — Cambridge University Press
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