CIA I ML Techniques .pdfsdasfafafasfsfasf

List of Course Work Subjects S.NO SEM SUBJECT CODE SUBJECT TITLE ELECTIVE/CORE CREDIT 1 1 22MC202 MACHINE LEARNING TECHNIQUES CORE 3 2 1 22PRM01 RESEARCH METHODOLOGY AND IPR CORE 3 3 1 22MC302 ADVANCED ARTIFICIAL INTELLIGENCE ELECTIVE 3 4 3 22MC209 ADVANCED INTERNET OF THINGS CORE 3 5 3 22PVD30 SYSTEM LEVEL HARDWARE SOFTWARE CODESIGN ELECTIVE 3 6 3 22MC324 INFORMATION RETRIEVAL TECHNIQUES ELECTIVE 3 22MC202 MACHINE LEARNING TECHNIQUES Course Objective 1. To introduce students to the basic concepts and techniques of Machine Learning. 2. To have a thorough understanding of the Supervised and Unsupervised learning techniques 3. To implement linear and non-linear learning models 4. To implement distance-based clustering techniques 5. To understand graphical models of machine learning algorithms Unit I FUNDAMENTALS OF MACHINE LEARNING 9 Learning – Types of Machine Learning – Supervised Learning – The Brain and the Neuron – Design a Learning System – Perspectives and Issues in Machine Learning – Concept Learning Task – Concept Learning as Search – Finding a Maximally Specific Hypothesis – Version Spaces and the Candidate Elimination Algorithm – Linear Discriminants – Perceptron – Linear Separability – Linear regression. Unit II LINEAR MODELS 9 Multi-layer Perceptron – Going Forwards – Going Backwards: Back Propagation Error – Multi-layer Perceptron in Practice – Examples of using the MLP – Overview – Deriving Back-Propagation – Radial Basis Functions and Splines – Concepts – RBF Network – Curse of Dimensionality – Interpolations and Basis Functions – Support Vector Machines Unit III DISTANCE-BASED MODELS 9 Nearest neighbor models – K-means – clustering around medoids – silhouettes – hierarchical clustering – Density based methods- Grid based methods- Advanced cluster analysis- k-d trees – locality sensitive hashing – non-parametric regression – bagging and random forests – boosting – meta learning Unit IV TREE AND RULE MODELS 9 Decision trees – learning decision trees – ranking and probability estimation trees – regression trees – clustering trees – learning ordered rule lists – learning unordered rule lists – descriptive rule learning – Mining Frequent patterns, Association and Correlations, advanced association rule techniques-first order rule learning Unit V REINFORCEMENT LEARNING AND GRAPHICAL MODELS 9 Reinforcement Learning – Overview – Getting Lost Example – Markov Decision Process, Markov Chain Monte Carlo Methods – Sampling – Proposal Distribution – Markov Chain Monte Carlo – Graphical Models – Bayesian Networks – Markov Random Fields – Hidden Markov Models – Tracking Methods. TOTAL HOURS: 45 PERIODS CO1 Understanding distinguish between, supervised, unsupervised and semi- supervised learning CO2 Apply the appropriate machine learning strategy for any given problem Course Outcome CO3 Suggestion of using supervised, unsupervised or semi-supervised leaRead less