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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