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Machine Learning -II
Course outcomes (Cos)
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Courseoutcome(atcourselevel) |
Learningandteaching Strategies |
AssessmentStrategies |
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On completion of this course, the students will be able to; CO 132.Recognize nonlinear problems in business domain and formulate them for analysis CO 133.Compare machine learning algorithms and select a suitable algorithm to handle nonlinear business problems. CO 134.Extract dataset and transform them for computation. CO 135.Design machine learning model to solve the problems and interpret their results CO 136.Analyse, synthesize and compare machine learning algorithms for business problems. CO 137.Evaluate the performance of machine learning models using ML metric like RMSE ,accuracy etc.
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Approach inteaching:Interactive Lectures,Group Discussion,Tutorials,CaseStudy
Learning activitiesfor the students:Self-learningassignments,presentations |
Class test,Semester endexaminations,Quiz,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.
• Advanced Machine Learning with Python, Hearty John,Packt (2016)
• Brian Boucheron , Lisa Tagliaferri,Machine Learning projects, DigitalOcean
Suggested readings
- McKinney, Python for Data Analysis. O’ Reilly Publication, 2017.
- Curtis Miller,” Hands-On Data Analysis with NumPy and Pandas",Packt, 2015
E resources
- InflibnetShodhganga, www.shodhganga.inflibnet.ac.in
- NPTEL Local Guru content
- https://nptel.ac.in/courses/106106139
- https://www.w3schools.com/
Journals
