Syllabus

• Week 1

  Recommender systems and their applications

  • Introduction to Recommender Systems

    Define a recommender system and identify why we need it.

  • Recommendation Approaches

    Identify different recommendation approaches.

  • Recommender Implementation and Evaluation

    Identify steps of building a recommender. Recognize how to evaluate a recommender.

  • Python Practice

    Install Python development environment and run Python programs.

  • Datasets

    Explore what a dataset is and why it is important to build a recommender system.

  • Lecture Notes and Source Code

    Download lecture notes and source code and explore them.

• Week 2

  Fundamental Recommenders

  • Data Collection

    Collect the data that we use to build a recommender. Look into the details of the data items.

  • Data Organization and Metrics

    Organize and prepare data for a recommender. Identify and design metrics for recommenders.

  • A Recommender based on Certain Metrics

    Build a recommender based on a certain metric.

  • A Recommender based on User’s Preferences

    Build a recommender based on user’s preferences.

  • A Recommender based on Similarities

    Build a recommender based on similarities.

• Week 3

  Content-based Recommender

  • Content-based Filtering

    Explore content-based filtering. Explore the dataset used to illustrate the content-based filtering.

  • A Content-based Recommender

    Explore the dataset again for a recommender. Design metrics for a recommender.

  • TF-IDF for a Recommender

    Calculate TF-IDF for a recommender.

  • A Content-based Recommender using TF-IDF

    Calculate cosine similarity for a recommender. Build a content-based recommender using TF-IDF.

• Week 4

  Collaborative Filtering Recommender

  • Collaborative Filtering

    Explore collaborative filtering

  • A User-Based CF Recommender

    Build a user-based collaborative filtering recommender

  • An Item-based CF Recommender

    Build a item-based collaborative filtering (CF) recommender

  • Matrix Factorization

    Explore matrix factorization and its role in collaborative filtering (CF) recommenders.

  • A Model-Based CF Recommender

    Build a model-based collaborative filtering recommender

• Week 5

  Artificial Intelligence (AI) and Machine Learning (ML)

  • AI, Machine and Deep Learning

    Explore AI, machine learning, and deep learning.

  • Machine Learning: Regression

    Use linear regression for prediction tasks.

  • Machine Learning: K-Means

    Use K-means to cluster data points.

  • Machine Learning: K-Nearest Neighbors (KNN)

    Use K-Nearest Neighbors (KNN) to classify data points.

  • Deep Learning

    Build a deep learning application.

• Week 6

  Machine Learning Recommender

  • Recommenders using Machine Learning

    Explore recommenders using machine learning.

  • A Recommender using Linear Regression

    Build a recommender using linear regression.

  • A Recommender using K-means

    Build a recommender using K-means.

  • A Recommender using K-Nearest Neighbors (KNN)

    Build a recommender using K-Nearest Neighbors (KNN)

  • A Recommender using Deep Learning

    Build a recommender using Neural Networks.

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