Machine Learning Course Outline

This is a list of topics which can be covered as part of machine learning course curriculum. In other words, it is a representation of outline of a machine learning course. This course outline is created by taking into considerations different topics which are covered as part of machine learning courses available on Coursera.org, Edx, Udemy etc. In case, you are planning to take up a machine learning course in near future, make sure that most of the following is covered.

An Outline to Machine Learning Course Curriculum

• Introduction to machine learning
• Regression
  • Linear Regression with One Variable
  • Linear Regression with Multiple Variables
• Logistic Regression
• Introduction to Neural Networks
  • Representation
  • Learning
• Support Vector Machines (SVM)
• Unsupervised learning (Clustering and Retrieval)
• Dimensionality reduction: Dimensionality reduction is the process of reducing the number of random variables under consideration[1] by obtaining a set of principal variables. Following topics needs to be covered:
  • Feature selection
  • Feature extraction: Introduction to different techniques such as some of the following for transforming data in high-dimensional space to a space of fewer dimensions.
    • Principal Component Analysis (PCA)
    • Kernal PCA
    • Graph based kernel PCA
    • Linear discriminant analysis (LDA)
    • Generalized discriminant analysis (GDA)
• Anomaly detection (also termed as Outlier detection)
  • Unsupervised anomaly detection
  • Supervised anomaly detection
  • Semi-supervised anomaly detection
• Recommender systems
  • Collaborative filtering
  • Content-based filtering
  • Hybrid recommender systems
• Machine Learning Examples/Case Studies

The following is a list of concepts which would be good to understand before one gets started with learning machine learning concepts.

• Introduction to Linear Algebra
• Regularization
• Introduction to probability and statistics