Wins - Course - AI and Machine Learning

This course outline covers the foundational theory, algorithms, core paradigms, and specialized domains of modern AI and Machine Learning.

1. Introduction to AI and Machine Learning

Foundational Concepts: Definition of AI, Machine Learning (ML), and Deep Learning (DL); historical evolution and state-of-the-art applications.
Mathematics for AI: Essential linear algebra (vectors, matrices, eigenvalues), calculus (gradients, partial derivatives), and probability/statistics (Bayes’ theorem, distributions).
Development Environment & Tools: Hands-on setup using Python, Jupyter Notebooks, NumPy, Pandas, Scikit-Learn, and cloud computing platforms.

Core Principles: Training models on labeled datasets to predict targets for unseen data.
Regression Analysis: Linear Regression, Multiple Regression, Polynomial Regression, and Regularization techniques (Lasso, Ridge).
Classification Algorithms: Logistic Regression, $k$ -Nearest Neighbors ( $k$ -NN), Support Vector Machines (SVM), Decision Trees, and Random Forests.
Model Evaluation & Optimization: Train/test splits, cross-validation, confusion matrices, precision/recall, ROC-AUC curves, and hyperparameter tuning (GridSearch, RandomSearch).

Core Principles: Extracting hidden patterns, structures, or representations from unlabeled data.
Clustering Techniques: $k$ -Means, Hierarchical Clustering, and DBSCAN for grouping similar data points.
Dimensionality Reduction: Principal Component Analysis (PCA) and t-SNE for feature extraction, compression, and visual data exploration.
Anomaly & Association Detection: Isolation Forests for outlier detection and Market Basket Analysis (Apriori algorithm).

Core Framework: The Agent-Environment interaction loop, states, actions, rewards, and policy optimization.
Markov Decision Processes (MDPs): Value functions, Bellman equations, and dynamic programming foundations.
Algorithmic Approaches: Model-free learning via Q-Learning, Deep Q-Networks (DQN), and Policy Gradient methods.
Real-World Applications: Autonomous systems, robotics, algorithmic trading, and game AI (e.g., AlphaGo).

Artificial Neural Networks (ANNs): Biological inspiration, perceptrons, activation functions (ReLU, Sigmoid, Softmax), forward propagation, and backpropagation.
Training Deep Models: Optimization algorithms (Stochastic Gradient Descent, Adam), loss functions, and preventing overfitting using Dropout and Batch Normalization.
Frameworks: Hands-on model building, training, and deployment using PyTorch or TensorFlow/Keras.

Text Preprocessing: Tokenization, stemming, lemmatization, stop-word removal, and vector space models (TF-IDF, Bag-of-Words).
Word Embeddings: Distributed representations including Word2Vec, GloVe, and FastText.
Sequential Architectures: Recurrent Neural Networks (RNNs), LSTMs, and GRUs for sequence processing.
Modern NLP & Large Language Models (LLMs): Attention mechanisms, Transformer architecture (Encoder/Decoder), BERT, GPT models, and Retrieval-Augmented Generation (RAG).

Image Processing Fundamentals: Image representation, filtering, edge detection, and feature extraction.
Convolutional Neural Networks (CNNs): Convolution layers, pooling layers, feature maps, and architectures (ResNet, VGG, EfficientNet).
Key Vision Tasks: Image classification, object detection (YOLO, Faster R-CNN), image segmentation, and facial recognition.
Generative Vision Models: Generative Adversarial Networks (GANs) and Diffusion Models for synthetic image generation.

Algorithmic Fairness & Bias: Identifying and mitigating societal, historical, and data bias in machine learning models.
Explainable AI (XAI): Model interpretability tools such as SHAP and LIME to audit black-box models.
Governance & Privacy: Data privacy frameworks, AI safety guidelines, copyright, and regulatory compliance.
Emerging Frontiers: Autonomous agents, multimodal models, neuromorphic computing, and quantum machine learning.