Course Content
Introduction to AI and Machine Learning
0/7
Overview of AI and ML – History and evolution of AI
00:00
Differences between AI, ML, and Data Science
00:00
Types of Machine Learning : Supervised Learning
00:00
Unsupervised Learning
00:00
Reinforcement Learning
00:00
Semi-supervised Learning
00:00
Applications of AI/ML
00:00
Mathematics for Machine Learning
0/10
Linear Algebra : Vectors, Matrices, and Tensors
00:00
Eigenvalues and Eigenvectors
00:00
Singular Value Decomposition
00:00
Calculus : Differentiation and Integration
00:00
Partial Derivatives and Gradient Descent
00:00
Probability and Statistics : Probability Theory
00:00
Distributions (Normal, Binomial, etc.)
00:00
Bayes’ Theorem
00:00
Optimization Techniques : Cost Function
00:00
Optimization Algorithms
00:00
Programming for AI/ML
0/4
Introduction to Python : Python basics and libraries
00:00
Data Handling : Numpy, Pandas, Matplotlib
00:00
Data Preprocessing and Cleaning
00:00
Introduction to Jupyter Notebooks
00:00
Supervised Learning
0/9
Linear Regression : Simple and Multiple Linear Regression
00:00
Evaluation Metrics
00:00
Classification Algorithms : Logistic Regression
00:00
Decision Trees
00:00
Support Vector Machines (SVM)
00:00
k-Nearest Neighbors (k-NN)
00:00
Model Evaluation : Confusion Matrix
00:00
Precision, Recall, F1-Score
00:00
ROC-AUC Curve
00:00
Unsupervised Learning
0/6
Clustering : k-Means Clustering
00:00
Hierarchical Clustering
00:00
DBSCAN
00:00
Dimensionality Reduction : Principal Component Analysis (PCA)
00:00
t-SNE
00:00
LDA
00:00
Neural Networks and Deep Learning
0/7
Introduction to Neural Networks : Perceptron Model
00:00
Activation Functions
00:00
Deep Learning : Multi-Layer Perceptrons (MLP)
00:00
Backpropagation
00:00
Optimization Techniques (Adam, RMSProp)
00:00
Convolutional Neural : Networks (CNNs) CNN Architecture
00:00
Applications in Image Processing
00:00
Reinforcement Learning
0/3
Introduction to Reinforcement Learning : Markov Decision Processes (MDPs)
00:00
Q-Learning and Deep Q-Networks (DQN)
00:00
Applications in Gaming and Robotics
00:00
AI/ML Model Deployment
0/3
Model Deployment Techniques : Flask, Django, FastAPI
00:00
Cloud Services : AWS, Google Cloud, Microsoft Azure
00:00
Monitoring and Scaling ML Models
00:00
AI/ML
About Lesson

Applications in gaming and robotics have significantly evolved, leveraging advanced technologies to create immersive and interactive experiences. In gaming, artificial intelligence (AI) is used to design dynamic, responsive environments and intelligent non-player characters (NPCs) that adapt to player behavior, enhancing gameplay and providing a more personalized experience. Virtual reality (VR) and augmented reality (AR) technologies have further transformed gaming by creating highly immersive worlds where players can interact with virtual elements in real time, making the experience more engaging and realistic.

In robotics, AI and machine learning algorithms are crucial in enabling robots to perceive their environment, make decisions, and perform tasks autonomously. Robotics applications span from industrial automation, where robots handle repetitive and complex tasks with high precision, to service robots used in healthcare, retail, and domestic environments for assisting humans in daily activities. Additionally, robotics in gaming often overlaps in areas like motion sensing and control, where players use robotic devices or sensors to interact with games physically, bridging the gap between virtual and real-world experiences.

Together, these applications in gaming and robotics are reshaping industries, driving innovation, and creating new opportunities for enhanced user engagement and automation.

Applications in Gaming and Robotics
Join the conversation
Previous
Next