Course Structure
The course is divided into 12 modules, each focusing on a specific aspect of AI. Each module includes lectures, practical exercises, projects, and assessments.
- Definition and history of AI
- AI vs. Machine Learning vs. Deep Learning
- Key concepts and terminology
- Applications of AI in various industries
- Current trends and future directions
- Introduction to machine learning
- Types of machine learning: supervised, unsupervised, and reinforcement learning
- Key algorithms: linear regression, logistic regression, k-nearest neighbors, decision trees
- Model evaluation and validation
- Practical implementation with Python and scikit-learn
- Ensemble methods: bagging, boosting, random forests
- Support Vector Machines (SVM)
- Clustering algorithms: k-means, hierarchical clustering
- Dimensionality reduction techniques: PCA, t-SNE
- Hyperparameter tuning and optimization
- Introduction to neural networks
- Architecture of neural networks: neurons, layers, activation functions
- Training neural networks: forward and backward propagation
- Deep learning frameworks: TensorFlow, Keras, PyTorch
- Building and training deep neural networks
- Basics of CNNs
- Convolutional and pooling layers
- Popular CNN architectures: LeNet, AlexNet, VGG, ResNet
- Image classification and object detection
- Practical implementation of CNNs
- Introduction to RNNs
- RNN architectures: vanilla RNNs, LSTMs, GRUs
- Applications of RNNs: time series prediction, sequence modeling
- Practical implementation of RNNs
- Attention mechanisms and transformers
- Overview of NLP
- Text preprocessing techniques
- Word embeddings: Word2Vec, GloVe, FastText
- NLP models: RNNs, LSTMs, BERT
- Text classification, sentiment analysis, language translation
- Introduction to computer vision
- Image preprocessing techniques
- Object detection and segmentation
- Image generation with GANs
- Practical applications and projects
- Basics of reinforcement learning
- Key concepts: agents, states, actions, rewards
- Popular algorithms: Q-learning, Deep Q-Networks (DQN), policy gradients
- Applications of reinforcement learning
- Practical implementation of reinforcement learning
- Ethical considerations in AI
- Bias and fairness in AI algorithms
- Privacy and data protection
- AI in society: impact on jobs, economy, and daily life
- Responsible AI development and deployment
- AI project lifecycle: problem definition, data collection, model development, deployment
- AI tools and platforms
- Case studies of AI applications in various industries
- Best practices for implementing AI solutions
- Planning and designing an AI project
- Data collection and preprocessing
- Model development and evaluation
- Deployment and presentation of the AI solution
- Final project review and feedback
Assessment and Certification
- Weekly quizzes and assignments
- Mid-term project: Develop a machine learning model for a specific problem
- Final capstone project: Create a comprehensive AI solution
- Participation in class discussions and practical exercises
- Certification awarded upon successful completion of the course
Prerequisites
- Basic understanding of programming (preferably Python)
- Familiarity with basic mathematics and statistics
