Lectures related to Online machine learning

Support Vector Machines: Interactive Class

Explores Support Vector Machines in machine learning, discussing SVM, support vectors, uniqueness of solutions, and multi-class SVM.

Stochastic Gradient Descent: Non-convex Optimization Techniques

Discusses Stochastic Gradient Descent and its application in non-convex optimization, focusing on convergence rates and challenges in machine learning.

Optimization Techniques: Stochastic Gradient Descent and Beyond

Discusses optimization techniques in machine learning, focusing on stochastic gradient descent and its applications in constrained and non-convex problems.

Classification Algorithms: Generative and Discriminative Approaches

Explores generative and discriminative classification algorithms, emphasizing their applications and differences in machine learning tasks.

Machine Learning Fundamentals: Structure Discovery, Classification, Regression

Covers fundamental machine learning concepts including Structure Discovery, Classification, and Regression.

Introduction to Machine Learning

Covers the basics of machine learning for physicists and chemists, focusing on image classification and dataset labeling.

Deep Neural Networks: Training and Optimization

Explores deep neural network training, optimization, preventing overfitting, and different network architectures.

Generalization Error

Explores generalization error in machine learning, focusing on data distribution and hypothesis impact.

Kernels: Nonlinear Transformations

Explores kernels for simplifying data representation and making it linearly separable in feature spaces, including popular functions and practical exercises.

Clustering: Principles and Methods

Covers the principles and methods of clustering in machine learning, including similarity measures, PCA projection, K-means, and initialization impact.

Deep and Convolutional Networks: Generalization and Optimization

Explores deep and convolutional networks, covering generalization, optimization, and practical applications in machine learning.

Machine Learning Fundamentals

Covers the fundamental concepts of machine learning, including classification, algorithms, optimization, supervised learning, reinforcement learning, and various tasks like image recognition and text generation.

Biases, ML performance and adversarial ML threats

Explores Machine Learning basics, adversarial conditions, privacy implications, and deployment challenges, highlighting biases and adversarial threats.

Pitfalls and Caveats in Machine Learning

Covers challenges in machine learning, emphasizing the importance of choosing relevant data and algorithms.

Machine Learning: Supervised and Unsupervised Learning Techniques

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Machine Learning at the Atomic Scale

Explores simple models, electronic structure evaluation, and machine learning at the atomic scale.

Deep Learning: Convolutional Networks

Explores convolutional neural networks, backpropagation, and stochastic gradient descent in deep learning.

Neural Networks: Training and Optimization

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Optimization in Machine Learning: Gradient Descent

Covers optimization in machine learning, focusing on gradient descent for linear and logistic regression, stochastic gradient descent, and practical considerations.

Optimization: Gradient Descent and Subgradients

Explores optimization methods like gradient descent and subgradients for training machine learning models, including advanced techniques like Adam optimization.