Lectures related to Machine Learning Basics

Covers clustering algorithms, evaluation methods, and practical applications in machine learning.

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.

Landscape and Generalisation in Deep Learning

Explores the challenges and insights of deep learning, focusing on loss landscape, generalization, and feature learning.

Machine Learning Fundamentals

Covers key concepts and examples of machine learning algorithms and techniques.

Clustering Principle: Feature Extraction and Similarity Measures

Covers clustering for feature extraction, similarity measures, outliers, and cluster shapes.

Clustering: Unsupervised Learning

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Data Preprocessing: Handling Challenges

Delves into advanced data preprocessing techniques, covering categorical encoding, missing data handling, and unbalanced datasets, emphasizing performance metrics and classifier comparison.

Clustering: Unsupervised Learning

Explores dimensionality reduction, clustering algorithms, and the state of machine learning.

Smart, Connected Products

Explores smart, connected products and their transformative impact on companies, covering artificial intelligence, machine learning, predictive models, forecasting methods, and more.

Dimensionality Reduction: PCA and LDA

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Time Series Clustering

Covers clustering time series data using dynamic time warping, string metrics, and Markov models.

Clustering: K-means & LDA

Covers clustering using K-means and LDA, PCA, K-means properties, Fisher LDA, and spectral clustering.

Policy Gradient and Actor-Critic Methods: Eligibility Traces Explained

Discusses policy gradient and actor-critic methods, focusing on eligibility traces and their application in reinforcement learning tasks.

Introduction to Machine Learning MOOC: IoT Systems and Industrial Applications with Design Thinking

Covers the basics of machine learning, focusing on supervised learning algorithms and real-world applications.

Understanding Deep Learning

Explores deep learning fundamentals, including image classification, neural network working principles, and machine learning challenges.

Deep learning in the IoT context MOOC: IoT Systems and Industrial Applications with Design Thinking

Delves into deep learning for image and object classification in IoT systems, including clustering techniques and privacy concerns.

Topic Models: Latent Dirichlet Allocation

Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.

Machine Learning Fundamentals

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

Advanced Design of Concrete Structures: Connections

Covers the advanced design of concrete structures focusing on the connections between elements.

Perception: Data-Driven Approaches

Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.

Machine Learning Basics

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