![[Pasted image 20240611142732.png|525]] # Machine Learning The “learning” in **machine learning** refers to trying to find hidden patterns within a set of data. There are In supervised learning, the data The actual concept of machine learning is extremely broad In unsupervised learning - *Exploratory data analysis:* We may want to explore the data without a particular end goal in mind. --- > [!abstract]+ **Common Terminology** > **Overfitting** is when a created model sticks too closely to the original data set and significantly lowers its accuracy when applied to data outside of the training set. > > **Hyperparameters** are external configuration variables that data scientists use > ## Types of Machine Learning | Type | Description | Example | | ------------------- | ----------- | ------------------------------------------ | | Classification | | Fraud detection & covid classification | | Clustering | | Customer segmentation & targeted marketing | | Dimension Reduction | | Single cell | | Regression | | | - Machine Learning - Unsupervised - Clustering - customer segmentation - targeted marketing - Dimensionality Reduction - structure discovery - meaningful compression - Supervised - Regression - sales forcasting - weather forcasting - Classification - fraud detection - covid classification - Reinforcement - Robot Navigation - Gaming AI - Random Forest (mean of multiple decision trees) - Support Vector Machines (SVM): plotting a linear line across potentially high dimensions of data - Canonical Correlation Analysis (CCA): **Deep Learning** - Most notable example are *neural networks* - Gets its name from the many different layers within the model that imitate the networks of neurons found within brain tissue - ## Supervised Learning - Given a data set of pairs - *Creates model* to predict future outcome from input data - Examples: - Linear Regression (square error) ### Types of Data Labels Labels can be ### Reinforcement Learning **Reinforcement Learning** - Based on rewarding desired behaviors and punishing undesired ones - [ ] vs adversarial neural networks? ## Unsupervised Learning - Given a data set and expected to discover notable patterns within it - Examples: - [[Clustering Algorithms|k-means clustering]] - [[Dimension Reduction|principal component analysis (PCA)]] A large majority (but not all) of unsupervised learning algorithms are non-deterministic ## Properties of Models ### Deterministic vs Probabilistic > See also: > - [[Probability Theory]] A machine learning model is **deterministic** if On the other hand, a model is called **probabilistic** if it - One example of a highly probabilistic model ## Components of Machine Learning Models ### Processing Input Data #### Transformers ### Domains - Source Domain: - Target Domain: