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.
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
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
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
(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
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