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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