guillainbisimwa / UdacityML

Intro to Machine Learning with Sebastian and Katie

ud120-projects

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

This repository contains project files for the Introduction to Udacity's Machine Learning Engineer Nanodegree program.

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

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 (SVM)

Idea, different types of errors, basic working principle, etc.

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

Deep learning

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

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

Author

👤 Guillain Bisimwa

• Github : @guillainbisimwa
• Twitter : @gullain_bisimwa
• Linkedin : guillain-bisimwa

🤝 Contributing

Contributions, issues, and feature requests are welcome!

Acknowledgments

• UDACITY

Show your support

Give a ⭐️ if you like this project!