Lecture 1 - Recommender system and its business value
Lecture 2 - Types of recommender system - I
Lecture 3 - Types of recommender system - II
Lecture 4 - Data Collection
Lecture 5 - Data Description
Lecture 6 - Data preprocessing
Lecture 7 - Dimensionality Reduction
Lecture 8 - Introduction to machine learning - I
Lecture 9 - Introduction to machine learning - II
Lecture 10 - Introduction to machine learning - III
Lecture 11 - Distance and Similarity
Lecture 12 - Distance and Similarity (Continued...)
Lecture 13 - User-Based Approach
Lecture 14 - Item-Based Approach
Lecture 15 - Additional Topics in Neighbourhood Based Approach
Lecture 16 - Association rule based model
Lecture 17 - UV Decomposition
Lecture 18 - The latent factor model
Lecture 19 - Basic latent factor models
Lecture 20 - Other advanced models
Lecture 21 - Introduction to content based recommender system: Foundations
Lecture 22 - Feature Engineering - I
Lecture 23 - Feature Engineering - II
Lecture 24 - Feature Engineering - III
Lecture 25 - Feature Engineering - IV
Lecture 26 - Decision Trees for content based recommendation
Lecture 27 - Naïve Bayes classifier for content based recommendation
Lecture 28 - kNN Classifier for Recommender System
Lecture 29 - Rule based classification
Lecture 30 - Regression methods and conclusions
Lecture 31 - Introduction to evaluation ofrecommender system
Lecture 32 - Resampling methods
Lecture 33 - Evaluation metrics for accuracy
Lecture 34 - Drawing reliable conclusions - I
Lecture 35 - Drawing reliable conclusions - II
Lecture 36 - Hybrid recommender systems
Lecture 37 - Knowledge based recommender systems
Lecture 38 - Context-Sensitive recommender systems
Lecture 39 - Structural Recommendations in Networks
Lecture 40 - Trust aware recommender systems
