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Related lectures (25)

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Support Vector Regression: Kernel Tricks

Explores Ridge and SVR regression, emphasizing kernel tricks for non-linear regression.

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.

Support Vector Machines: Interactive Class

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

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Graph Machine Learning

Delves into graph-enhanced machine learning, focusing on fraud detection, malware detection, and recommendation systems.

Word Embeddings: Context and Representation

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

Quantum Machine Learning: Theory and Applications

Explores quantum machine learning, representations of molecules, kernel regression, and the interplay between physics and machine learning.

Analyzing Hebbian Learning Rule

Explores rate-based Hebbian learning, covariance rules, and weight vector growth in neural networks.