Machine Learning

  Clustering and  Principal Component Analysis Unsupervised Learning  Concepts: Clustering Algorithms (K-Means, Hierarchical Clustering) Principal Component Analysis (PCA) Anomaly Detection Model Evaluation and Selection Model Performance Metrics Cross-Validation Techniques Hyperparameter Tuning Model Selection Techniques What is Unsupervised Learning? Unsupervised learning is a type of machine learning where the algorithm is trained on unlabeled data to identify hidden patterns or structures.  Unsupervised learning is a machine learning technique where the goal is to discover patterns or relationships in data without any labelled information. The data is unlabeled, and the algorithm must find structure within the data on its own. Clustering is a common unsupervised learning technique used to group similar data points together.

Analytical  and  Explanation-based learning  with domain theories  Analytical Learning Concepts Introduction Learning with perfect domain theories: PROLOG-EBG Explanation-based learning Explanation-based learning of search control knowledge Analytical Learning Definition :  Analytical learning is a type of machine learning that uses statistical and mathematical techniques to analyze and make predictions based on data.

Sequential Covering Algorithms and  Learning sets of First-Order rules:  Set of Rules :  Learning a set of rules is a type of machine learning that focuses on discovering patterns or rules that explain the data.  Introduction Sequential covering algorithms Learning First-Order rules Learning sets of First-Order rules: FOIL Summary Introduction  Learning sets of rules is a type of machine learning that involves learning a set of rules from data that can be used to make predictions or classifications. One popular approach to learning sets of rules is through the use of sequential covering algorithms.

Computational Machine Learning and  learning an approximately correct hypothesis Contents of Computational Learning Introduction Probably learning an approximately correct hypothesis Sample complexity for finite hypothesis space Sample complexity for infinite hypothesis space The mistake-bound model of learning Computational Learning Theory:  Computational learning theory is a field of machine learning that studies the mathematical properties and limitations of learning algorithms.

k-Nearest Neighbor Learning and  Locally Weighted Regression  Instance-Based Learning Topics Introduction k -Nearest Neighbor Learning Locally Weighted Regression Radial Basis Functions Case-Based Reasoning Remarks on Lazy and Eager Learning. What is Instance-based Learning? Instance-based learning is a type of machine learning that uses a lazy learning approach, where the algorithm stores the training examples and makes predictions based on similarity measures between the new instance and the stored instances.  Introduction Instance-Based Learning is a type of machine learning where the algorithm is given a dataset, and it learns by memorizing the instances in the dataset. The algorithm uses these instances to make predictions on new, unseen instances. The key idea behind instance-based learning is that the function approximator only makes predictions based on the similarity between the new instance and the instances in the dataset.

SVM  Efficient optimization with SMO algorithm Support Vector Machines Topics Separating data with the maximum margin Finding the maximum margin, Efficient optimization with SMO algorithm Speeding up optimization with full Platt SMO Using Kernels for More Complex Data Dimensionality Reduction Techniques: Principal Component analysis What is Support Vector Machines (SVM)? SVM is a type of machine learning algorithm that finds a hyperplane in a high-dimensional space to maximize the margin between the classes.