Syllabus Machine Learning - (314443) Credit Scheme : Examination Scheme : 03 Credits Mid_Semester : 30 Marks End_Semester : 70 Marks Unit I INTRODUCTION TO MACHINE LEARNING Introduction : What is Machine Learning, Definition, Real life applications, Learning Tasks- Descriptive and Predictive Tasks, Types of Learning : Supervised Learning Unsupervised Learning, Semi-Supervised Learning, Reinforcement Learning. Features : Types of Data (Qualitative and Quantitative), Scales of Measurement (Nominal, Ordinal, Interval, Ratio), Concept of Feature, Feature construction, Feature Selection and Transformation, Curse of Dimensionality. Dataset Preparation : Training Vs. Testing Dataset, Dataset Validation Techniques - Hold-out, k-fold Cross validation, Leave-One-Out Cross-Validation (LOOCV). (Chapter - 1) Unit II CLASSIFICATION Binary Classification : Linear Classification model, Performance Evaluation- Confusion Matrix, Accuracy, Precision, Recall, ROC Curves, F-Measure. Multi-class Classification : Model, Performance Evaluation Metrics - Per-class Precision and Per-Class Recall, weighted average precision and recall -with example, Handling more than two classes, Multiclass Classification techniques -One vs One, One vs Rest. Linear Models : Introduction, Linear Support Vector Machines (SVM) - Introduction, Soft Margin SVM, Introduction to various SVM Kernel to handle non-linear data - RBF, Gaussian, Polynomial, Sigmoid. Logistic Regression - Model, Cost Function. (Chapter - 2) Unit III REGRESSION Regression : Introduction, Univariate Regression - Least-Square Method, Model Representation, Cost Functions : MSE, MAE, R-Square, Performance Evaluation, Optimization of Simple Linear Regression with Gradient Descent - Example. Estimating the values of the regression coefficients. Multivariate Regression : Model Representation. Introduction to Polynomial Regression : Generalization- Overfitting Vs. Underfitting, Bias Vs. Variance. (Chapter - 3) Unit IV TREE BASED AND PROBABILISTIC MODELS Tree Based Model : Decision Tree - Concepts and Terminologies, Impurity Measures -Gini Index, Information gain, Entropy, Tree Pruning -ID3/C4.5, Advantages and Limitations. Probabilistic Models : Conditional Probability and Bayes Theorem, Naïve Bayes Classifier, Bayesian network for Learning and Inferencing. (Chapter - 4) Unit V DISTANCE AND RULE BASED MODELS Distance Based Models : Distance Metrics (Euclidean, Manhattan, Hamming, Minkowski Distance Metric), Neighbors and Examples, K-Nearest Neighbour for Classification and Regression, Clustering as Learning task : K-means clustering Algorithm-with example, k-medoid algorithm-with example, Hierarchical Clustering, Divisive Dendrogram for hierarchical clustering, Performance Measures. Association Rule Mining : Introduction, Rule learning for subgroup discovery, Apriori Algorithm, Performance Measures - Support, Confidence, Lift. (Chapter - 5) Unit VI INTRODUCTION TO ARTIFICIAL NEURAL NETWORK Perceptron Learning - Biological Neuron, Introduction to ANN, McCulloch Pitts Neuron, Perceptron and its Learning Algorithm, Sigmoid Neuron, Activation Functions : Tanh, ReLu. Multi-layer Perceptron Model - Introduction, Learning parameters : Weight and Bias, Loss function : Mean Square Error. Introduction to Deep Learning (Chapter - 6)
