AI & Machine Learning

Artificial Intelligence & Machine Learning Study Guide

Week 1-2: Introduction to AI and Machine Learning

What is AI? History, applications, and key concepts.
Types of AI: Narrow AI vs. General AI vs. Superintelligent AI.
Key Concepts in AI: Agents, environments, goals, and actions.
Overview of Machine Learning: Supervised, unsupervised, and reinforcement learning.
Applications of AI and ML: Natural Language Processing, Computer Vision, Robotics, etc.
Mathematical Foundations: Linear Algebra, Probability, and Statistics.

Week 3-4: Supervised Learning

Regression Problems: Linear Regression, Polynomial Regression.
Classification Problems: Logistic Regression, K-Nearest Neighbors (KNN), Naive Bayes.
Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC and AUC.
Overfitting and Underfitting: Bias-variance tradeoff, cross-validation.
Model Optimization: Gradient Descent, Regularization (L1, L2).

Week 5-6: Unsupervised Learning

Clustering Algorithms: K-Means, Hierarchical Clustering, DBSCAN.
Dimensionality Reduction: Principal Component Analysis (PCA), t-SNE.
Association Rule Learning: Apriori, Eclat.
Anomaly Detection: Isolation Forest, One-Class SVM.

Week 7-8: Neural Networks and Deep Learning

Artificial Neural Networks (ANNs): Architecture, Activation Functions, Feedforward Neural Networks.
Backpropagation and Gradient Descent: Training neural networks, learning rate.
Deep Learning: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs).
Optimizers: SGD, Adam, RMSProp.
Introduction to Frameworks: TensorFlow, Keras, PyTorch.

Week 9-10: Reinforcement Learning (RL)

Basics of Reinforcement Learning: Agents, States, Actions, Rewards.
Markov Decision Process (MDP): States, actions, rewards, transition model.
Value Iteration & Policy Iteration: Dynamic programming for RL.
Q-Learning: Model-free reinforcement learning.
Deep Q Networks (DQN): Combining deep learning and reinforcement learning.

Week 11-12: Advanced Topics in AI/ML

Generative Models: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs).
Transfer Learning: Fine-tuning pre-trained models.
Attention Mechanisms & Transformers: Attention, Self-attention, BERT, GPT models.
Natural Language Processing (NLP): Text Preprocessing, Word Embeddings, Sequence-to-Sequence models.
Ethics in AI: Bias, fairness, transparency, accountability.

Week 13-14: Practical Applications and Case Studies

AI in Industry: Healthcare, Finance, Autonomous Vehicles, Gaming, etc.
Model Deployment: Model serving, APIs, containerization (Docker, Kubernetes).
Scalability: Cloud computing, distributed ML.
Case Studies: Real-world AI/ML applications in business and society.
Capstone Project: Final project that combines the concepts learned throughout the course.

Week 15: Review and Final Exam

Review: Recap of key concepts, algorithms, and techniques.
Exam: Final exam covering all topics.
Capstone Project Presentation: Presenting your final project to the class.

Recommended Textbooks and Resources

Books:
- Artificial Intelligence: A Modern Approach by Stuart Russell and Peter Norvig
- Pattern Recognition and Machine Learning by Christopher Bishop
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
Online Resources:
- Coursera, edX, and Udacity courses (e.g., Andrew Ng’s Machine Learning Course, Deep Learning Specialization)
- Kaggle (competitions, datasets, and notebooks)
- Google AI, OpenAI, and other research papers and blogs.

Tools and Libraries

Python Libraries:
- Scikit-Learn, Pandas, NumPy (for data manipulation and ML algorithms).
- Matplotlib, Seaborn (for data visualization).
- TensorFlow, Keras, PyTorch (for deep learning).
Other Tools:
- Jupyter Notebooks (for interactive coding).
- GitHub (version control for code).
- Docker, Kubernetes (for deployment).

This syllabus is designed to be flexible depending on the time available and the depth of coverage desired. The balance of theory and practice will depend on the course’s objectives (e.g., academic vs. hands-on).

AI & ML