/Udacity-Machine-Learning-Nanodegree

All projects and lecture notes of the Udacity Machine Learning Engineer Nanodegree.

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Udacity - Machine Learning Nanodegree

Udacity's Machine Learning Nanodegree project files and lecture notes.

This repository contains project files and lecture notes for Udacity's Machine Learning Engineer Nanodegree program which I started working on in March 2018.

Lecture note reference

Model evaluation and validation

Topics covered in this section:

  • Model Evaluation
    Confusion matrix, F1 score, F-beta score, ROC curve
  • Model selection
    Types of errors, various types of cross validation, learning curves, grid search

See lecture notes: here

Supervised learning

Topics covered in this section:

  • Linear regression
    Absolute trick, advantages / disadvantages, L1 regularisation, L2 regularisation
  • Decision trees
    Entropy, information gain, hyperparameters
  • Naive bayes
    Prior probability, posterior probability, naive bayes
  • Support vector machines
    Idea, different types of errors, basic working principle, etc.

See lecture notes: here

Unsupervised learning

Topics covered in this section:

  • Clustering
    K-means clustering
  • Hierarchical and density-based clustering
    Hierarchical clustering, single-link clustering, complete-link clustering, average-link clustering, ward's method, DB scan
  • Gaussian mixture model and cluster validation
    EM algorithm, cluster validation, external indices, internal indices, adjusted rand indices, silhouette coefficient
  • Feature scaling
  • PCA
  • Random projection and ICA
    Johnson-Lindenstrauss lemma, ICA, applications

See lecture notes: here

Deep learning

(Less comprehensive due to my prior knowledge)

Topics covered in this section:

  • Neuronal networks
    Perceptron trick, perceptron algorithm, sigmoid activation, maximum likelihood, cross entropy, logistic regression, perceptron and gradient descent
  • Deep neural networks
    Regularization, dropout, vanishing gradients and activation function, momentum, keras optimisers
  • Convolutional neural networks
    Model validation, image augmentation

See lecture notes: here

Reinforcement learning

Topics covered in this section:

  • RL framework
    Reinforcement setting, episodic and continuous tasks, rewards hypothesis, cumulative reward, discounted reward, Markov decision process, Bellman equations, optimality, action-value functions,
  • Dynamic programming
    Iterative policy evaluation, estimation of action values, policy improvement, policy iteration, truncated policy iteration, value iteration
  • Monte Carlo methods
    Predicting state values, estimating action-values, incremental mean, policy evaluation, policy improvement, exploration-exploitation dilemma, GLIE MC control algorithm, constant-alpha GLIE MC control algorithm
  • Temporal difference learning
    TD(0) prediction, action value estimation, solving the control problem, Sarsamax (Q-learning), expected Sarsa
  • Deep reinforcement learning
    Discrete and continuous spaces, discretization, coarse coding, tile coding, function approximation, kernel functions, coarse coding
  • Deep Q-Learning
    NNs as value functions, Monte Carlo learning, TD learning, Q-learning, Sarsa vs. Q-learning, experience replay, fixed Q-targets, different types of DQNs
  • Policy-based methods
    Policy function approximation, stochastic policy search, policy gradients, Monte Carlo policy gradients, constrained policy gradients
  • Actor-critic methods

See lecture notes: here