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Lecture
Optimization in Machine Learning
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Related lectures (47)
Deep Learning: Multilayer Perceptron and Training
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Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.
Linear Models for Classification: Logistic Regression and SVM
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Covers linear models for classification, focusing on logistic regression and support vector machines.
Word Embeddings: Modeling Word Context and Similarity
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Covers word embeddings, modeling word context and similarity in a low-dimensional space.
Deep Learning: Convolutional Neural Networks and Training Techniques
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Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.
Statistical Learning: Fundamentals
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Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.
Neural Networks for NLP
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Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.
Regularization in Machine Learning
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Introduces regularization techniques to prevent overfitting in machine learning models.
Decision Trees and Random Forests: Concepts and Applications
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Discusses decision trees and random forests, focusing on their structure, optimization, and application in regression and classification tasks.
Deep Learning: Designing Neural Network Models
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Covers the design and optimization of neural network models in deep learning.
Feed-forward Networks
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Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.
Gradient Descent
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Covers the concept of gradient descent, a universal algorithm used to find the minimum of a function.
Text Models: Word Embeddings and Topic Models
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Explores word embeddings, topic models, Word2vec, Bayesian Networks, and inference methods like Gibbs sampling.
Linear Regression and Logistic Regression
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Covers linear and logistic regression for regression and classification tasks, focusing on loss functions and model training.
Lovász Local Lemma: Basics
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Covers the basics of the Lovász Local Lemma, including mutually independent bad events and pseudoprobabilities.
Sparse Regression
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Covers the concept of sparse regression and the use of Gaussian additive noise in the context of MAP estimator and regularization.
Machine Learning Basics
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Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.
Machine Learning and Privacy
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Explores Federated Machine Learning and Differential Privacy in Machine Learning, discussing attacks, defenses, and challenges.
Adaptive Signal Processing: Filtering & Neural Networks
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Explores adaptive signal processing, gradient descent, and the LMS algorithm for efficient filtering and neural network training.
Untitled
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Channel Coding and BICM (LLRs)
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Explores channel coding, BICM, and LLRs in wireless communication systems, emphasizing the importance of error detection and correction.
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