Department Events
MACHINE LEARNING
This course enables the students to 1. Understand the basic concepts, approaches and techniques in Machine Learning 2. Comprehend concept of supervised and unsupervised learning & other advanced learning models. 3. Understand modern notions in data analysis oriented computing 4. Learn to evaluate machine learning models 5. Relate and apply the learned algorithms to a real-world problem, optimize the models learned and report on the expected accuracy that can be achieved by applying the models. Course Outcomes(COs): Learning Outcome (at course level) Learning and teaching strategies Assessment Strategies CO224. To acquaint with the foundations of machine learning CO225. Understand working concept of different types of supervised models. CO226. Explore Unsupervised learning technique based machine learning CO227. Study different probabilistic graph based models and their applications CO228. Learn advanced machine learning techniques and the problem domain where these can be applied CO229. Understand how to evaluate models generated from data CO230. Apply the algorithms to real-world problems CO231. Interpret & evaluate the results of models learned CO232. Apply gained knowledge to research in Machine Learning Approach in teaching: Interactive Lectures, Modeling, Discussions, implementing enquiry based learning, student centered approach, Research problem based discussions Learning activities for the students: Experiential Learning, Presentations, Discussions, Quizzes and Assignments · Assignments · Written test in classroom · Classroom Activity · Continuous Assessment · Semester End Examination
Introduction Machine Learning, Machine Learning Foundations-Overview, Applications, Types of Machine Learning, Basic Concepts in Machine Learning – Examples of Machine Learning, Perspectives/Issues in Machine Learning, AI vs. Machine Learning, Introduction to Python.
Supervised Learning Introduction, Linear Models of Classification – Decision Trees, Naïve Bayes Classification, Linear Regression – Logistic Regression – Bayesian Logistic Regression – Probabilistic Models Neural Network-Feed Forward Network Functions – Error Back Propagation – Regularization - Bayesian Neural Networks – Radial Basis Function Networks, Ensemble Methods – Random Forest – Bagging – Boosting.
Unsupervised Learning Clustering, K-Means Clustering, EM (Expectation Maximization), Mixtures of Gaussians, EM algorithm in General, The Curse of Dimensionality, Dimensionality Reduction, Factor Analysis, Principal Component Analysis, Probabilistic PCA, Independent Component Analysis. Challenges for Big Data Analytics.
Probabilistic Graphical Models Directed Graphical Models, Bayesian Networks, Exploiting Independence Properties, From Distributions to Graphs, Examples – Markov Random Fields – Inference In Graphical Models – Learning - Naïve Bayes Classifiers – Markov Models – Hidden Markov Models. Undirected graphical Models – Markov Random Fields – Conditional Independence Properties.
Advanced Learning Sampling – Basic Sampling Method – Monte Carlo, Reinforcement Learning-Introduction-The Learning Task, Instance based Learning-Nearest neighbor classification, k-nearest neighbor, nearest neighbor error probability, Elements of Reinforcement Learning, Difference between Reinforcement Learning and Supervised Learning, Applications of Reinforcement Learning, Model based learning, Semi-Supervised Learning, Computational Learning Theory.
