AI course matterial

Here is a comprehensive overview of AI course material, structured to take a learner from absolute beginner to advanced concepts. This is a typical progression used in university courses and online specializations.

Overall Learning Path

1. Prerequisites → 2. Intro to AI & Python → 3. Math for AI → 4. Core AI Concepts → 5. Machine Learning (The Heart of Modern AI) → 6. Deep Learning → 7. Advanced & Specialized Topics → 8. Capstone Project

1. Prerequisites

• Programming: Basic proficiency in any language. Python becomes essential later.

• Mathematics: Comfort with high-school level algebra, logic, and basic calculus is highly beneficial.

2. Introduction to Artificial Intelligence (Overview Course)

• Key Concepts:

  • What is AI? Definitions and goals.

  • The history of AI: From the Dartmouth Conference to the modern era.

  • Types of AI: Narrow (Weak) AI vs. General (Strong) AI.

  • The Turing Test and other measures of intelligence.

  • Major subfields and applications (e.g., NLP, Computer Vision, Robotics).

• Tools: None, or light Python for basic examples.

3. Python for AI/Data Science (Crash Course)

• Key Concepts/Libraries:

  • NumPy: The fundamental package for scientific computing with Python (arrays, linear algebra).

  • Pandas: Data manipulation and analysis (DataFrames, Series).

  • Matplotlib & Seaborn: Data visualization (plotting graphs and charts).

  • Basic Python: Control flow, functions, classes, and working with libraries.

• Project: Clean, analyze, and visualize a real-world dataset (e.g., from Kaggle).

4. Mathematics for Artificial Intelligence

This is often woven into other courses but is crucial for understanding why algorithms work.

• Linear Algebra: Vectors, matrices, matrix multiplication, determinants, eigenvalues, SVD (Singular Value Decomposition). Essential for understanding data representation and neural networks.

• Calculus: Derivatives, gradients, partial derivatives, the chain rule. The foundation of how models learn via gradient descent.

• Probability & Statistics: Probability rules, Bayes' Theorem, probability distributions, mean, variance, standard deviation, correlation. Essential for dealing with uncertainty and evaluating models.

5. Core AI Concepts (Traditional Symbolic AI)

• Search Algorithms:

  • Uninformed Search (BFS, DFS, Uniform Cost Search)

  • Informed Search (Greedy Search, A* Search)

  • Game Playing (Minimax Algorithm, Alpha-Beta Pruning)

• Knowledge Representation & Reasoning:

  • Logic (Propositional, First-Order)

  • Planning (STRIPS, PDDL)

• Constraint Satisfaction Problems (CSPs):

  • Definition, backtracking, constraint propagation.

6. Machine Learning (The Core of Most Modern AI)

This is typically a multi-course sequence.

• A. Fundamentals:

  • Types of Learning: Supervised, Unsupervised, Reinforcement Learning.

  • Key Concepts: Model training, testing, validation, overfitting/underfitting, bias-variance tradeoff.

  • Library: scikit-learn

• B. Supervised Learning:

  • Regression: Predicting continuous values (Linear Regression, Polynomial Regression, Regularization - Ridge/Lasso).

  • Classification: Predicting categories (Logistic Regression, k-Nearest Neighbors (k-NN), Support Vector Machines (SVM), Decision Trees, Naive Bayes).

• C. Unsupervised Learning:

  • Clustering: Grouping data (k-Means, Hierarchical Clustering, DBSCAN).

  • Dimensionality Reduction: Reducing features (PCA - Principal Component Analysis, t-SNE).

• D. Model Evaluation:

  • Metrics: Accuracy, Precision, Recall, F1-Score, ROC-AUC curve (for classification); MSE, R-squared (for regression).

  • Techniques: Train/Test Split, Cross-Validation.

7. Deep Learning (A Subset of ML using Neural Networks)

• Key Concepts/Libraries:

  • Libraries: TensorFlow / Keras or PyTorch (industry standards).

  • Neural Network Fundamentals: Perceptrons, activation functions (Sigmoid, ReLU, Tanh), loss functions, gradient descent, backpropagation.

• Core Architectures:

  • Convolutional Neural Networks (CNNs): For image data (filters, pooling). Architectures: LeNet, AlexNet, VGG, ResNet.

  • Recurrent Neural Networks (RNNs): For sequence/time-series data (LSTMs, GRUs). Applications: text generation, machine translation.

  • Transformers: The modern architecture for NLP (Attention Is All You Need paper). Basis for models like BERT, GPT, and LLMs (Large Language Models).

• Topics: Transfer Learning, Data Augmentation, Hyperparameter Tuning.

8. Advanced & Specialized Topics (Electives)

• Natural Language Processing (NLP): Tokenization, TF-IDF, Word Embeddings (Word2Vec, GloVe), Named Entity Recognition (NER), Sentiment Analysis, modern Transformer models (BERT, GPT).

• Computer Vision: Image classification, object detection (YOLO, R-CNN), image segmentation, facial recognition.

• Reinforcement Learning (RL): Agents, environments, rewards, policies, value functions, Q-Learning, Deep Q-Networks (DQN). Applications: game AI (AlphaGo), robotics.

• Generative AI:

  • Generative Adversarial Networks (GANs): Generating realistic images, videos, and data.

  • Diffusion Models: The technology behind state-of-the-art image generators like DALL-E, Midjourney, and Stable Diffusion.

  • Large Language Models (LLMs): Prompt engineering, fine-tuning, and responsible AI.

• AI Ethics: Bias and fairness in AI, explainable AI (XAI), accountability, and societal impact.

9. Capstone Project

• The culmination of the learning journey.

• Goal: Solve a significant, real-world problem by integrating knowledge from multiple areas.

• Examples:

  • Build a custom image classifier for a specific type of object.

  • Create a chatbot using NLP techniques.

  • Develop a recommendation system for movies or products.

  • Train an agent to play a simple game using RL.

Recommended Online Courses & Resources

• Beginner:

  • Google's AI Crash Course: Free, great high-level overview.

  • Elements of AI (University of Helsinki): Free, non-technical introduction.

• Intermediate/Advanced (The "Gold Standard" Sequences):

  • Andrew Ng's Courses (Coursera): The classic entry point.

    • Machine Learning (uses Octave/Matlab)

    • AI For Everyone (non-technical)

    • Deep Learning Specialization (5 courses, uses Python & TensorFlow)

  • Fast.ai: Top-down, practical approach to deep learning (very highly regarded).

  • CS229 & CS231n (Stanford): The famous university courses (lectures are on YouTube). Very math-heavy and theoretical.

• Specializations:

  • Coursera: Deep Learning.AI specializations, IBM AI Engineering.

  • edX: MIT, Harvard, and other university MicroMasters programs.

• Books:

  • "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron (the practical bible).

  • "Pattern Recognition and Machine Learning" by Christopher M. Bishop (the theoretical bible).

  • "Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig (the definitive textbook on broader AI concepts).