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