Machine Learning
6IT4-02 · Semester 6
0/49 topics
Objective
Scope
Outcome
Introduction to Machine Learning
Types of learning
Machine learning applications
Supervised learning overview
Linear Regression model
Naive Bayes classifier
Decision Tree algorithm
K-Nearest Neighbor algorithm
Logistic Regression
Support Vector Machine (SVM)
Random Forest algorithm
Grouping unlabelled items using k-means clustering
Hierarchical clustering
Probabilistic clustering
Association rule mining
Apriori algorithm
FP-growth algorithm
Gaussian mixture model
Feature extraction concept
Principal Component Analysis (PCA)
Singular Value Decomposition (SVD)
Feature selection concept
Feature ranking
Subset selection
Filter methods
Wrapper methods
Embedded methods
Evaluating Machine Learning algorithms
Model Selection
Semi-supervised learning
Reinforcement learning
Markov Decision Process (MDP)
Bellman equations
Policy evaluation using Monte Carlo
Policy iteration
Value iteration
Q-Learning
State-Action-Reward-State-Action (SARSA)
Model-based Reinforcement Learning
Collaborative filtering
Content-based filtering
Artificial Neural Networks (ANN)
Perceptron
Multilayer network
Backpropagation
Introduction to Deep Learning