Lectures related to Recommender system

Recommender Systems: Basics and Techniques

Covers collaborative filtering and content-based methods for recommender systems, addressing cold start problems and making predictions.

Recommender Systems

Explores the evolution and impact of recommender systems, covering information retrieval, collaborative filtering, and different recommendation algorithms.

Recommender Systems

Explores recommender systems, collaborative filtering, content-based recommendations, similarity metrics, and advanced methods like matrix factorization.

Recommender Systems: Text Classification & Naïve Bayes

Explores text classification using Naïve Bayes in content-based recommenders.

Matrix Factorization: Optimization and Evaluation

Explores matrix factorization optimization, evaluation methods, and challenges in recommendation systems.

Recommender Systems: Personalized Recommendations for Users

Delves into challenges of content personalization, focusing on web recommendations and personalized newsletters.

Recommender Systems: MovieLens Dataset

Covers implementing recommender systems using the MovieLens dataset and evaluating them with RMSE and MAE metrics.

Recommender Systems: Part 1

Introduces recommender systems, collaborative filtering, content-based recommendation, similarity metrics, and matrix factorization.

Optimization in Machine Learning

Explores optimization techniques, word embeddings, and recommendation systems in machine learning.

Recommender Systems: Overview and Methods

Explores the evolution of recommenders, collaborative filtering, Netflix Prize, model training, and optimization techniques.

Recommender Systems: Matrix Factorization & Evaluation

Explores matrix factorization techniques for recommender systems, including evaluation metrics like RMSE and NDCG.

Recommender Systems and Structure Discovery

Explores recommender systems, latent factor models, and clustering algorithms for structure discovery.

Gaussian Mixture Models: Applications in Recommender Systems

Explores the applications of Gaussian Mixture Models in recommender systems and real-world scenarios like the Roomba robot.

Projects Presentation & Logistics

Covers the presentation of 4 projects in the course and related logistics.

Personalized Music Recommendations: Enhancing User Experience

Explores personalized music recommendations to enhance user experience through metadata and user input.

Foundations of Information Systems: Course Overview and Key Concepts

Introduces the course on information systems, covering its structure, objectives, and foundational concepts essential for understanding data management and decision-making.

Neuroscience Dataset Recommendation

Covers a tutorial on recommending similar neuroscience datasets using knowledge embeddings.

K-Means Algorithm: Implementation and Application as Recommendation System

Delves into implementing the K-Means algorithm, evaluating performance, and applying it as a recommendation system.

KNN: Nearest Neighbor Classifier

Covers the k-Nearest-Neighbor classifier, hand-written digit recognition, multi-class k-NN, data reduction, applications, graph construction, limitations, and the curse of dimensionality.

Introduction to Machine Learning

Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.