Foundation of Machine Learning
- 1. Foundation to Machine Learning Waziri Shebogholo University of Dodoma April 10, 2019 Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 1 / 20
- 2. Truth Machines don’t learn. Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 2 / 20
- 3. Machine Learning Subfield of computer science that is concerned with building algorithms, which rely on the collection of examples of the world problems to become useful. Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 3 / 20
- 4. Applications Text classification Natural Language Processing Computer vision tasks, eg. Image recognition, face recognition Medical diagnosis Recommendation systems Games eg. AlphaGo Speech recognition eg. Siri, Google Home Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 4 / 20
- 5. Types of Machine Learning Machine Learning problems can be:- 1 Supervised Learning 2 Unsupervised Learning 3 Reinforcement Learning Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 5 / 20
- 6. Supervised Learning Terminologies Example: an object or instance of data used Dataset: collection of examples Features: set of attributes, often represented as vector (feature vector), associated with an object Labels/Target 1 In classification, category associated with an example 2 In regression, real-valued numbers Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 6 / 20
- 7. Supervised Learning Goal The goal of Supervised Learning algorithm is to use the dataset to create a model that takes feature vector as input and output information that allows to deduce a label for that feature vector. Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 7 / 20
- 8. Unsupervised Learning Unlabeled examples Goal The goal of Unsupervised Learning is to create a model that take a feature vector and either transform it into another vector or a value that can be used to solve a problem. Clustering, model return cluster id Dimensionalty reduction, model return a feature vector that have fewer features Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 8 / 20
- 9. Reinforcement Learning Think of a machine in a certain environment that is able to perceive the state of that environment as vector of features. The machine can execute actions in every state. Different actions brings different rewards and could also move the machine from one state to another. Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 9 / 20
- 10. Goal The goal of Reinforcement Learning algorithm is to learn a policy. Policy is a function that takes the feature vector of a state and output an optimal action to execute in that state. Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 10 / 20
- 11. How it works? Example Let’s consider a problem in which we want to predict whether a person has cancer or not. Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 11 / 20
- 12. How it works? 1 Let’s frame this as supervised learning problem in which we first start with gathering data. 2 Data for supervised learning problems is a collection of pairs inputs, outputs After having data, we need to choose a Learning algorithm . Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 12 / 20
- 13. How it works? Model y = wx + b Goal The goal of Learning algorithm is to leverage the dataset and find optimal values of parameters w and b. How? Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 13 / 20
- 14. How it works? Optimization Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 14 / 20
- 15. What you’ll hear the most with regard to a model? 1 Parameters 2 Hyperparameters Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 15 / 20
- 16. What you’ll hear the most with regard to a model? Parameters are variables that define the model learned by the learning algorithm. They are modified by the learning algorithm based on the training data. We train models to find these variables. Hyperparameter is a property of learning algorithm mostly, having a numerical value. They influence the way the algorithm works. Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 16 / 20
- 17. Fundamental Algorithms 1 Linear regression 2 Logistic regression 3 Decision tree 4 Random Forest * 5 Support Vector Machine 6 k-Nearest Neighbors 7 Neural Networks Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 17 / 20
- 18. Linear Regression Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 18 / 20
- 19. Best practices Feature Engineering Learning algorithm selection Data splitting Underfitting and overfitting Regularization Model performance assesment Hyperparameter tuning Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 19 / 20
- 20. Thank you. Waziri Shebogholo (University of Dodoma) Foundation to Machine Learning April 10, 2019 20 / 20