Week 1: Basic Mathematics for Machine Learning
•Linear Algebra: Vectors, matrices, matrix operations
•Calculus: Derivatives and gradients, optimization concepts
•Probability and Statistics: Distributions, mean, variance, and Bayes’ theorem
Week 2: Introduction to Machine Learning
•Overview of ML concepts and types (supervised vs. unsupervised)
•Python libraries for ML (NumPy, Pandas, Matplotlib)
•Data preprocessing and exploration techniques
Week 3: Supervised Learning
•Regression Algorithms: Linear Regression, Polynomial Regression
•Classification Algorithms: Logistic Regression, Decision Trees, SVM
•Evaluation metrics (Confusion Matrix, Precision, Recall, F1-Score)
Week 4: Unsupervised Learning
•Clustering techniques (K-Means, DBSCAN, Hierarchical)
•Dimensionality reduction (PCA, t-SNE)
•Hands-on lab: Implementing clustering algorithms
Week 5: Introduction to Deep Learning
•Basics of neural networks (architecture, activation functions)
•Training neural networks (loss functions, backpropagation)
•Implementing a simple neural network with Keras
Week 6: Model Selection and Capstone Project
•Hyperparameter tuning methods (Grid Search, Random Search)
•Final project: End-to-end ML solution presentation
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Peer reviews and feedback

Week 1: Basic Mathematics for AI
•Logic and Set Theory: Foundations of logic, predicates, quantifiers
•Probability Theory: Conditional probability, Bayes’ theorem
•Optimization Techniques: Gradient descent, convex vs. non-convex functions
Week 2: Introduction to AI
•AI vs. ML vs. DL, history and evolution of AI
•Applications of AI in various industries
•Ethical considerations in AI
Week 3: Problem-Solving and Search Algorithms
•Introduction to search algorithms (BFS, DFS, A*)
•Optimization techniques and heuristics (hill climbing, simulated annealing)
•Hands-on coding exercises
Week 4: Knowledge Representation
•Logic and reasoning in AI (Propositional Logic, Predicate Logic)
•Ontologies and semantic networks
•Real-world case studies
Week 5: Introduction to Machine Learning Techniques
•Overview of supervised vs. unsupervised learning
•Introduction to deep learning concepts and applications
•Case studies: Practical implementations of ML in AI
Week 6: AI Project Development
•Capstone project: Develop an AI-based solution integrating ML techniques
•Presentations and peer feedback
•Discussion on future trends in AI

Week 1: Basic Mathematics for Machine Learning
•Linear algebra (vectors, matrices, operations)
•Calculus (derivatives, gradients)
•Probability and statistics basics (distributions, mean, variance)
Week 2: Introduction to MATLAB for ML
– Overview of MATLAB environment
– Data manipulation and visualization
– Introduction to the Statistics and Machine Learning Toolbox
Week 3: Supervised Learning in MATLAB
– Implementing regression and classification algorithms
– Cross-validation techniques
– Hands-on project: Building a classifier
Week 4: Unsupervised Learning in MATLAB
– Clustering algorithms and implementations
– Anomaly detection
– Practical session: Clustering real-world data
Week 5: Model Selection and Tuning
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Hyperparameter tuning methods (Grid Search, Random Search)
•Cross-validation techniques
•Practical session: Model evaluation
Week 6: Ensemble Methods and Capstone Project
•Introduction to ensemble learning (Bagging, Boosting)
•Final project: End-to-end ML solution presentation
•Peer reviews and feedback

Week 1: Basic Mathematics for Image Processing
•Linear algebra for images (pixel representation, transformations)
•Basic statistics for image analysis
•Concepts of Fourier transforms
Week 2: Introduction to Digital Image Processing
•Basics of image formation and representation
•MATLAB for image processing: Image Acquisition Toolbox
•Image manipulation techniques
Week 3: Image Enhancement Techniques
•Spatial and frequency domain processing
•Filtering and enhancement techniques
•Hands-on lab: Applying filters to images
Week 4: Image Segmentation and Feature Extraction
•Techniques for image segmentation (thresholding, clustering)
•Feature extraction methods
•Practical session: Segmentation task
Week 5: Image Restoration and Reconstruction
•Techniques for image restoration (deblurring, denoising)
•Introduction to image inpainting
•Hands-on project: Restoring images
Week 6: Capstone Project and Applications
•Applications in various domains (medical, industrial)
•Final project: Develop an image processing application
•Presentations and feedback session

Week 1: Basic Mathematics for Deep Learning
•Linear algebra fundamentals (matrix operations)
•Basics of calculus (derivatives, backpropagation)
•Probability and statistics in neural networks
Week 2: Introduction to Deep Learning
•Basics of neural networks and deep learning
•MATLAB’s Deep Learning Toolbox overview
•Building and training a simple neural network
Week 3: Convolutional Neural Networks (CNNs)
•Structure and function of CNNs
•Implementing CNNs for image classification
•Hands-on project: Build a CNN model
Week 4: Recurrent Neural Networks (RNNs)
•Understanding RNN architecture
•Applications in time-series analysis and natural language processing
•Practical session: Build an RNN model
Week 5: Transfer Learning and Fine-tuning
•Introduction to transfer learning concepts
•Fine-tuning pre-trained models
•Hands-on lab: Applying transfer learning
Week 6: Final Project and Deployment
•Best practices in model training and evaluation
•Capstone project: Develop a deep learning solution
•Final presentations and discussions

Week 1: Basic Mathematics for Deep Learning
•Linear algebra (vectors, matrices, tensors)
•Calculus fundamentals (gradients, chain rule)
•Basics of probability and statistics
Week 2: Introduction to Deep Learning
•Overview of deep learning and its applications
•Python libraries for deep learning (TensorFlow, Keras)
•Building your first neural network
Week 3: Convolutional Neural Networks (CNNs)
•CNN architecture and applications
•Implementing CNNs using Keras
•Hands-on project: Image classification
Week 4: Recurrent Neural Networks (RNNs)
•RNN architecture and use cases
•Sequence prediction with LSTMs
•Practical exercises: Text generation
Week 5: Transfer Learning and Fine-tuning
•Utilizing pre-trained models
•Techniques for fine-tuning
•Hands-on lab: Applying transfer learning
Week 6: Final Project and Best Practices
•Model evaluation and optimization
•Capstone project: Create a deep learning model for a specific problem
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Presentations, peer reviews, and feedback