Lectures related to Optimization in Machine Learning

Handling Text: Document Retrieval, Classification, Sentiment Analysis

Explores document retrieval, classification, sentiment analysis, TF-IDF matrices, nearest-neighbor methods, matrix factorization, regularization, LDA, contextualized word vectors, and BERT.

Word Embeddings: Context and Representation

Explores word embeddings, emphasizing word-context relationships and low-dimensional representations.

Word Embeddings: Models and Learning

Explores word embeddings, context importance, and learning algorithms for creating new representations.

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Logistic Regression: Probability Modeling and Optimization

Explores logistic regression for binary classification, covering probability modeling, optimization methods, and regularization techniques.

Matrix Factorization: Optimization and Evaluation

Explores matrix factorization optimization, evaluation methods, and challenges in recommendation systems.

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Latent Semantic Indexing

Covers Latent Semantic Indexing, word embeddings, and the skipgram model with negative sampling.

Word Embedding Models: Optimization and Applications

Explores optimizing word embedding models, including loss function minimization and gradient descent, and introduces techniques like Fasttext and Byte Pair Encoding.

Deep Learning: Multilayer Perceptron and Training

Covers deep learning fundamentals, focusing on multilayer perceptrons and their training processes.

Document Retrieval and Classification

Covers document retrieval, classification, sentiment analysis, and topic detection using TF-IDF matrices and contextualized word vectors like BERT.

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Word Embeddings: Modeling Word Context and Similarity

Covers word embeddings, modeling word context and similarity in a low-dimensional space.

Multilayer Perceptron: Training and Optimization

Explores the multilayer perceptron model, training, optimization, data preprocessing, activation functions, backpropagation, and regularization.

Deep Learning: Convolutional Neural Networks and Training Techniques

Discusses convolutional neural networks, their architecture, training techniques, and challenges like adversarial examples in deep learning.

Statistical Learning: Fundamentals

Introduces the fundamentals of statistical learning, covering supervised learning, decision theory, risk minimization, and overfitting.

Neural Networks for NLP

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Probabilistic Linear Regression

Explores probabilistic linear regression, covering joint and conditional probability, ridge regression, and overfitting mitigation.

Text Handling: Matrix, Documents, Topics

Explores text handling, focusing on matrices, documents, and topics, including challenges in document classification and advanced models like BERT.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.