Machine Learning Syllabus

This article is created to cover the topic-wise syllabus of Machine Learning. Let's start with its introduction.

Introduction to Machine Learning

  • Introduction of Machine Learning
  • Types of Learning:
    • Supervised Learning
    • Unsupervised Learning
    • Semi-supervised Learning
    • Reinforcement Learning
  • Well defined learning problems
  • Designing a Learning System
  • History of Machine Learning
  • Approaches:
    • Artificial Neural Network
    • Clustering
    • Decision Tree Learning
    • Bayesian networks
    • Support Vector Machine
    • Genetic Algorithm
  • Issues in Machine Learning
  • Data Science Vs Machine Learning

Regression

  • Linear Regression and Logistic Regression
  • Bayesian Learning:
    • Bayes theorem
    • Concept learning
    • Bayes Optimal Classifier
    • Naive Bayes classifier
    • Bayesian belief networks
    • Expectation-Maximization (EM) algorithm

Support Vector Machine (SVM)

  • Introduction to SVM
  • Types of support vector kernel:
    • Linear kernel
    • polynomial kernel
    • Gaussian kernel
  • Hyperplane
  • Properties of SVM
  • Issues in SVM

Decision Tree Learning

  • Decision tree learning algorithm
  • Inductive bias
  • Inductive inference with decision trees
  • Entropy and information theory
  • Information gain
  • Iterative Dichotomiser-3(ID3) Algorithm
  • Issues in Decision tree learning
  • Instance-based Learning:
    • k-Nearest Neighbour Learning
    • Locally Weighted Regression
    • Radial basis function networks
    • Case-based learning

Artificial Neural Networks (ANN)

  • What is Neural Network?
  • Overview of Biological Neurons:
    • Structure of biological neurons relevant to ANNs
  • Fundamental Concepts of Artificial Neural Networks:
    • Models of ANNs
    • Feedforward & feedback networks
    • Learning rules:
      • Hebbian learning rule
      • perception learning rule
      • delta learning rule
      • Widrow-Hoff learning rule
      • correction learning rule
      • Winner –lake all elarning rule
  • Single layer Perception Classifier:
    • Classification model
    • Features & Decision regions
    • Training & classification using discrete perceptron
    • Algorithm
    • Single layer continuous perceptron networks for linearly seperable classifications
  • Multi-layer Feed forward Networks:
    • Linearly non-seperable pattern classification
    • Delta learning rule for multi-perceptron layer
    • Generalized delta learning rule
    • Error back-propagation training
    • Learning factors
  • Single layer feed back Networks:
    • Basic Concepts
    • Hopfield networks
    • Training & Examples
  • Associative memories:
    • Linear Association
    • Basic Concepts of recurrent Auto associative memory:
      • Rentrieval algorithm
      • Storage algorithm
  • By directional associative memory
  • Architecture
  • Association encoding & decoding
  • Stability
  • Self organizing networks:
    • Unsupervised learning of clusters
    • Winner-take-all learning
    • Recall mode
    • Initialisation of weights
    • Seperability limitations

Deep Learning

  • Introduction
  • Neural Networks:
    • Introduction to Neural Networks
    • Gradient Descent
    • Training of Neural Networks
    • Sentiment Analysis
    • Deep Learning with Pytorch
  • Convolutional Neural Networks:
    • Cloud Computing
    • Convolutional Neural Network
    • CNNs in PyTorch
    • Weight Initialization
    • Autoencoders
    • Transfer Learning in PyTorch
    • Deep Learning for Cancer Detection
  • Recurrent Neural Networks:
    • Recurrent Neural Networks
    • Long Short-Term Memory Network
    • Implementation of RNN and LSTM
    • Hyperparameters
    • Embeddings and Word2vec
    • Sentiment Prediction RNN
  • Generative Adversarial Networks:
    • Generative Adversarial Network
    • Deep Convolutional GANs
    • PIX2PIX and Cyclegan
  • Updating a Model:
    • Introduction to Deployment
    • Deploy a Model
    • Custom Models and Web Hosting
    • Model Monitoring
    • Updating a Model
  • Examples of deep learning projects
  • Case study of CNN for eg on Diabetic Retinopathy, Building a smart speaker, Self-deriving car etc.

Reinforcement Learning

  • Introduction to Reinforcement Learning
  • Learning Task
  • Example of Reinforcement Learning in Practice
  • Learning Models for Reinforcement:
    • Markov Decision process
    • Q Learning
    • Q Learning function
    • Q Learning Algorithm
  • Application of Reinforcement Learning
  • Introduction to Deep Q Learning
  • Genetic Algorithm:
    • Introduction
    • Components
    • GA cycle of reproduction
    • Crossover
    • Mutation
    • Genetic Programming
    • Models of Evolution and Learning
    • Applications

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