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Machine Learning -II
Course Objectives: This course will enable students to Course Outcomes (COs): Course Course outcome (at course level) Learning and teaching strategies Assessment Strategies Paper Code Paper Title CLO 129.Recognize nonlinear problems in business domain and formulate them for analysis CLO 130.Compare machine learning algorithms and select a suitable algorithm to handle nonlinear business problems. CLO 131.Extract dataset and transform them for computation. CLO 132.Design machine learning model to solve the problems and interpret their results CLO 133.Analyse, synthesize and compare machine learning algorithms for business problems. CLO 134.Evaluate the performance of machine learning models using ML metric like RMSE ,accuracy etc. Approach in teaching: Interactive Lectures, Group Discussion, Tutorials, Case Study Learning activities for the students: Self-learning assignments, Machine Learning exercises, presentations Class test, Semester end examinations, Quiz, Practical Assignments, Presentation
Principal component analysis, employing PCA using python Self-organizing maps, employing SOM using python
Concept of Artificial Neural Networks, Types of neural networks, MLP, KNN, Restricted Boltzmann Machine, toplogy, training and applications of RBM. Implementation of MLP,KNN and RBM using python
Deep belief networks, deep learning, applying and validating DBN, implementing deep learning using python, Autoencoders, denoising and applying autoencoders and assessing performance
Ensemble methods, bagging algorithms and random forest, employing random forest using python. Introduction to prescriptive analysis and recommendation system.
Case studies: Bike Sharing trends, customer segmentation and effective cross selling, analyzing wine types and quality, forecasting stock and commodity prices.
