Machine Learning Syllabus for MAKAUT

Unit 1: Supervised Learning

Regression
Classification
Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes
Linear models: Linear Regression, Logistic Regression, Generalized Linear Models
Support Vector M…


This content originally appeared on DEV Community and was authored by Oni

Unit 1: Supervised Learning

  • Regression
  • Classification
  • Basic methods: Distance-based methods, Nearest-Neighbours, Decision Trees, Naive Bayes
  • Linear models: Linear Regression, Logistic Regression, Generalized Linear Models
  • Support Vector Machines (SVM)
  • Nonlinearity and Kernel Methods
  • Beyond Binary Classification: Multi-class, Structured Outputs, Ranking

Unit 2: Unsupervised Learning

  • Clustering: K-means, Kernel K-means
  • Dimensionality Reduction: PCA (Principal Component Analysis), Kernel PCA
  • Matrix Factorization and Matrix Completion
  • Generative Models: Mixture models and Latent Factor Models

Unit 3: Evaluating ML Algorithms & Model Selection

  • Evaluation of Machine Learning Algorithms
  • Model Selection
  • Introduction to Statistical Learning Theory
  • Ensemble Methods: Boosting, Bagging, Random Forests

Unit 4: Advanced Modeling

  • Sparse Modeling and Estimation
  • Modeling Sequence/Time-Series Data
  • Deep Learning and Feature Representation Learning

Unit 5: Scalable and Advanced ML Topics

  • Scalable Machine Learning
    • Online Learning
    • Distributed Learning
  • Advanced Topics (selection among):
    • Semi-supervised Learning
    • Active Learning
    • Reinforcement Learning
    • Inference in Graphical Models
    • Introduction to Bayesian Learning and Inference

Unit 6: Recent Trends

  • Recent trends in machine learning learning techniques
  • Recent trends in classification methods


This content originally appeared on DEV Community and was authored by Oni


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