Data Science + Design Thinking - Perfect Blend to Achieve the Best User Experience - Michael Radwin
Blend data science with design thinking to create AI/ML products that prioritize customer needs, using empathy, experimentation, and rapid prototyping for optimal user experience.
A Review of Machine Learning Techniques for Anomaly Detection - Dr. David Green
Explore machine learning techniques for anomaly detection, covering traditional methods, deep neural networks, and practical applications in various domains.
Machine Learning with DataRobot AI Cloud Platform
Comprehensive tutorial on DataRobot AI Cloud Platform, covering machine learning model building, evaluation, and deployment. Explores key features like AutoML, bias detection, and model optimization.
Classification Algorithm Tutorial - Machine Learning Tutorial
Comprehensive tutorial on classification algorithms in machine learning, covering techniques for categorizing new observations based on training data. Explores supervised learning methods and practical applications.
Statistics, Topology and Data Analysis
Explore the synergy between statistics and topological data analysis for solving real-world problems, with a focus on supervised learning and applications in materials science.
At Scale Anomaly Detection for Enterprise Security - Joshua Neil, Microsoft
Explore scalable anomaly detection for enterprise cybersecurity with Microsoft's Joshua Neil. Learn about real-time telemetry, unsupervised learning, and post-breach detection techniques.
Exact Multi-Parameter Persistent Homology of Time-Series Data
Explore multi-parameter persistent homology for time-series data analysis, featuring an exact formula for one-dimensional reduction and efficient computational methods using Fourier decomposition and Liouville torus.
Deep Learning and Computations of PDEs by Siddhartha Mishra
Explore deep learning applications in PDEs, uncertainty quantification, and optimization. Learn about operator learning, DeepOnet decomposition, and high-dimensional PDEs in this advanced lecture.
Using Machine Learning to Formulate Mathematical Conjectures - IPAM at UCLA
Explore how machine learning aids mathematicians in formulating conjectures, focusing on a discovery linking low-dimensional topology and geometry, with insights on the method's potential and limitations.
Approximation Theory of Deep Learning from the Dynamical Viewpoint
Explore deep learning approximation theory from a dynamical perspective, covering supervised learning, universal approximation, sequence modeling, and insights on various neural network architectures.
Building a State Space for Song Learning
Explore how songbirds develop neural sequences for vocalizations, examining the role of auditory memory in shaping motor circuits and facilitating song learning through reinforcement mechanisms.
Python for Machine Learning - Machine Learning Tutorial for Beginners
Comprehensive introduction to Python libraries for machine learning, covering NumPy, Pandas, and Seaborn. Includes practical implementations of algorithms and data visualization techniques.
Python for Machine Learning - Machine Learning for Beginners
Explore essential Python libraries for machine learning, including NumPy for numerical computation, Pandas for data manipulation, and Seaborn for visualization. Gain practical skills through demos and Q&A.
Python for Machine Learning - Machine Learning Tutorial for Beginners
Learn Python for machine learning, covering key concepts, popular packages, and algorithms like linear regression and K-nearest neighbor. Gain practical skills for a high-paying career in this rapidly growing field.
Python for Machine Learning - Machine Learning Tutorial - Python Tutorial
Explore Python libraries for machine learning, including NumPy, Pandas, and Seaborn. Learn data manipulation, visualization, and practical ML implementation through examples and hands-on demonstrations.
Machine Learning Mid-Semester Review - Lecture 15
Dive into a comprehensive mid-semester review of fundamental machine learning concepts, covering key principles and techniques explored throughout the academic term.
Machine Learning Fundamentals - Lecture 10
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.
Machine Learning Fundamentals - Lecture 1
Dive into foundational machine learning concepts, algorithms, and theoretical frameworks in this comprehensive first lecture of advanced data science studies.
Introduction to Neural Networks - Lecture 25
Explore neural networks' fundamental concepts, structure, and prediction mechanisms while learning essential training approaches for building effective machine learning models.
Machine Learning Fundamentals - Lecture 7
Dive into advanced machine learning concepts and algorithms through comprehensive coverage of key theoretical foundations and practical implementations in data science.
Machine Learning Fundamentals - Lecture 6
Dive into advanced machine learning concepts and techniques through comprehensive coverage of key algorithms, theoretical foundations, and practical applications in data science.