B.E Artificial Intelligence and Machine Learning Recommendation Systems Lab Syllabus - Mumbai University

Module 1

• Implementation of Matrix operations and data representation towards understanding mathematics for recommendation system

Module 2: Experiment on the role of clustering methods with respect to recommendation systems

Module 3: Feature engineering and pre-processing of data for recommendation systems.

Module 4: Implementation of Bayes classifier for recommendation.

Module 5: Implement User-based Nearest neighbor recommendation.

Module 6: Implement Item-based Nearest neighbor recommendation

Module 7: Implement Content-based recommendation system.

Module 8: Implement Knowledge-based recommendation system.

Module 9: Implementation of a recommendation system using Hybrid approach.

Module 10: Implementation of a recommendation system using Ensembled approach.

Module 11: Implementation of a Regression based recommendation system.

Module 12

• Analyze results on the basis of different evaluation parameters and graphical representations for recommendation systems.

Module 13: Mini Project Report: For any one chosen real world Recommendation systems application.

Module 14: Implementation and Presentation of Mini Project

Useful Links

• 1 https://towardsdatascience.com/recommendation-systems-explained-a42fc60591ed
• 2 https://www.coursera.org/specializations/recommender-systems