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Semester 2: Machine Learning
Introduction to Machine Learning: Concepts, examples, performance evaluation, cross-validation
Introduction to Machine Learning
Concepts
Machine Learning is a subset of artificial intelligence that focuses on building systems that learn from data to improve their performance over time. It involves algorithms that recognize patterns and make decisions based on input data.
Examples
Common examples of machine learning include recommendation systems, image recognition, speech recognition, and predictive analytics. These applications utilize supervised, unsupervised, and reinforcement learning techniques.
Performance Evaluation
To assess machine learning models, performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC are used. These metrics help determine the effectiveness and reliability of the model.
Cross-Validation
Cross-validation is a technique used to validate the performance of a machine learning model by splitting the dataset into training and testing subsets. Common methods include k-fold cross-validation, which helps ensure that the model is robust and generalizes well to unseen data.
Decision Trees and Bayesian Learning: Random forests, Naive Bayes classifier, Instance-based learning
Decision Trees and Bayesian Learning
Decision Trees
Decision trees are a type of supervised learning algorithm used for classification and regression tasks. They work by splitting the dataset into smaller subsets based on feature values. Each split corresponds to a decision node, and the final output is represented as leaf nodes. Decision trees are easy to interpret and visualize but can be prone to overfitting.
Random Forests
Random forests are an ensemble learning method that uses multiple decision trees to improve prediction accuracy and control overfitting. In a random forest, each tree is trained on a different subset of the data, using a technique called bootstrap sampling. The final output is determined by averaging the predictions (for regression) or taking a majority vote (for classification) from all the trees.
Naive Bayes Classifier
The Naive Bayes classifier is a family of probabilistic algorithms based on Bayes' theorem. It assumes that the presence of a particular feature in a class is independent of the presence of other features. Naive Bayes is particularly effective for high-dimensional datasets and is commonly used in text classification tasks such as spam detection.
Instance-Based Learning
Instance-based learning is a type of learning where instances of the training data are stored, and predictions are made based on examples rather than generalizing from a model. Techniques like k-Nearest Neighbors (k-NN) fall under this category. The main advantage of instance-based learning is its simplicity and effectiveness for certain types of problems, but it can be computationally expensive for large datasets.
Artificial Neural Networks: Perceptrons, backpropagation, linear models, PCA and Kernel PCA
Artificial Neural Networks: Perceptrons, Backpropagation, Linear Models, PCA and Kernel PCA
Introduction to Artificial Neural Networks
Overview of artificial neural networks and their significance in machine learning. Understanding the basic structure of an artificial neuron and how neural networks mimic the human brain.
Perceptrons
Explanation of the perceptron model, which is the simplest type of artificial neural network. Discussion on how perceptrons can be used for binary classification tasks and their limitations.
Backpropagation
Detailed discussion on the backpropagation algorithm used for training neural networks. Explanation of how this algorithm adjusts weights through a gradient descent approach to minimize the error.
Linear Models
Introduction to linear models in the context of machine learning. Discussion on linear regression as a foundation for more complex methods and its applications.
Principal Component Analysis (PCA)
Overview of PCA as a dimensionality reduction technique. Explanation of how PCA transforms data into a lower-dimensional space while preserving variance.
Kernel PCA
Extension of PCA that uses kernel methods to capture non-linear relationships in data. Discussion on the advantages of Kernel PCA over traditional PCA.
SVM and Kernel Methods: Formulation, linear and nonlinear classifiers, polynomial and radial kernels
SVM and Kernel Methods
Introduction to SVM
Support Vector Machines (SVM) are supervised learning models used for classification and regression tasks. They work by finding the optimal hyperplane that separates different classes in the feature space.
Formulation of SVM
The optimization problem in SVM aims to maximize the margin between different classes while penalizing misclassifications. The formulation includes Lagrange multipliers and constraints for correct classification.
Linear Classifiers
Linear classifiers use a linear decision boundary to separate classes. SVM becomes a linear classifier when data is linearly separable in the input space. It finds the hyperplane that best separates the classes.
Nonlinear Classifiers
For data that is not linearly separable, SVM can use kernel methods to transform the data into a higher-dimensional space where a linear separation is possible.
Kernel Methods
Kernels are functions that compute the inner product of two data points in a transformed feature space without explicitly mapping the data points into that space. This is known as the 'kernel trick'.
Polynomial Kernels
Polynomial kernels allow SVM to model nonlinear relationships by using polynomial functions to compute the similarity between data points. The degree of the polynomial can be adjusted for complexity.
Radial Basis Function Kernels
Radial Basis Function (RBF) kernels, also known as Gaussian kernels, measure similarity based on the distance between data points. RBF kernels are popular due to their effectiveness in various scenarios.
Conclusion
SVM and kernel methods are powerful tools in machine learning for both linear and nonlinear classifications. The choice of kernel plays a critical role in the model's performance.
Deep Learning: Neural networks, convolutional nets, autoencoders, recurrent networks and use cases
Deep Learning: Neural networks, convolutional nets, autoencoders, recurrent networks and use cases
Neural Networks
Neural networks are computational models inspired by the human brain. They consist of layers of interconnected nodes, or neurons, which process input data and can learn patterns through backpropagation. Applications include image recognition, speech recognition, and natural language processing.
Convolutional Neural Networks (CNNs)
CNNs are specialized neural networks designed for processing structured grid data such as images. They use convolutional layers to automatically detect features such as edges and textures. Applications include object detection, image segmentation, and video analysis.
Autoencoders
Autoencoders are unsupervised neural networks used for dimensionality reduction and feature learning. They consist of an encoder that compresses input data and a decoder that reconstructs it. Applications include anomaly detection, image denoising, and representation learning.
Recurrent Neural Networks (RNNs)
RNNs are designed for sequential data processing. They have connections that allow information to persist over time, making them suitable for tasks like time series prediction, language modeling, and speech processing. Variants include Long Short-Term Memory (LSTM) networks.
Use Cases
Use cases for deep learning span across various domains: healthcare (medical image analysis), finance (fraud detection), autonomous vehicles (scene understanding), and entertainment (recommendation systems). Each application leverages the strengths of different types of neural networks.
