Pattern Recognition and Application
This course covers feature extraction techniques and the representation of patterns in feature space. Measure of similarity between two patterns. Statistical, nonparametric and neural network techniques for pattern recognition have been discussed in this…
An Introduction to Artificial Intelligence
Comprehensive exploration of AI fundamentals, covering search algorithms, game theory, logic, uncertainty, Bayesian networks, and reinforcement learning, with historical context and practical applications.
Reinforcement Learning
Explore Q-learning, deep Q networks, and reinforcement learning environments through hands-on tutorials and practical implementations.
Natural Language Processing
This course provides an understanding of natural language processing, its tools, techniques, philosophy and principle. Topics covered include sound, words and word forms, structures, meaning, and web 2.0 applications
Pattern Recognition
Comprehensive exploration of statistical pattern recognition, covering Bayes classifiers, density estimation, linear discriminants, neural networks, SVMs, and advanced learning techniques.
Deep Learning
Comprehensive exploration of deep learning concepts, from feature descriptors to advanced neural networks, covering theory and practical applications in computer vision and generative models.
Self-Driving Cars With Carla and Python
In this course, you will learn about Carla, an open source autonomous driving simulator. Carla creates a sort of open world with streets, other cars, pedestrians, weather, sensors, and more to help you to write logic to drive self-driving cars, coming wi…
Pattern Recognition
This course provides a fairly comprehensive view of the fundamentals of pattern classification and regression. Topics covered in the lectures include an overview of pattern classification and regression; Bayesian decision making and Bayes classifier
Fundamentals of Artificial Intelligence
Comprehensive exploration of AI concepts, from problem-solving and search algorithms to knowledge representation, reasoning, planning, decision-making, and various machine learning techniques.
Applied Natural Language Processing
Explore natural language processing techniques, from corpus operations to advanced neural networks, for applications in machine translation, topic modeling, and conversation systems.
Machine Learning
The course places machine learning in its context within AI and gives an introduction to the most important core techniques such as decision tree-based inductive learning, inductive logic programming, reinforcement learning and deep learning through deci…
Natural Language Processing
Accelerated introduction to NLP, covering machine learning basics, text preprocessing, vectorization, and advanced neural network architectures like RNNs, LSTMs, and Transformers.
Advanced Digital Signal Processing - Multirate and Wavelets
Explore advanced digital signal processing concepts, focusing on multirate systems and wavelets. Learn about filter banks, time-frequency analysis, and various wavelet transforms with practical applications.
Gatsby Tutorial
In this Gatsby tutorial series we'll learn Gatsby from the ground up to create a dynamic, multi-page Gatsby portfolio website. We'll learn about SSG, GraphQL, Pages, Routes, Plugins and much more.
Computer Vision
Explore machine learning and computer vision fundamentals, advanced neural network architectures, and applications in object detection and semantic segmentation.
Digital Speech Processing
Explore digital speech processing, from production and perception to analysis techniques like LPC and STFT. Learn about phonetics, speech synthesis, and recognition for technology development.
NVIDIA Jetson Xavier NX Machine Learning Tutorials
Comprehensive tutorials on NVIDIA Jetson Xavier NX, covering setup, camera integration, object tracking, and face recognition. Ideal for beginners and advanced users in machine learning and computer vision.
Emergence of Life in the Brain
Explore the potential emergence of a new form of life within the brain, examining neural matter, replicators, and the intersection of evolution and learning.
David Wolpert on the No Free Lunch Theorems and Why They Undermine the Scientific Method
Explore the No Free Lunch Theorems and their implications for science, intelligence, and modeling. Gain insights into the relationship between mathematics and physics, and the limitations of universal problem-solving strategies.
When Computers Look at Art
Explore how AI and computer vision revolutionize art analysis, offering new insights into paintings' style, meaning, and historical context while addressing challenges in image understanding.
AI- Friend or Foe? AlphaGo's Impact on the World of Go
Explores AlphaGo's impact on Go strategy, professional players, and the game's future, discussing AI's role in revealing new techniques and challenging traditional approaches.