Unit 925: Introduction to Machine Learning and AI Techniques

1. Introduction to Machine Learning

• Definitions and basic concepts
• Types of machine learning
  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning
• Common applications of machine learning

2. Linear Regression

• Concept and purpose
• Assumptions of linear regression
• Model fitting techniques
  • Ordinary Least Squares
• Evaluation metrics
  • Mean Squared Error (MSE)
  • R-squared
• Interpretation of regression results

3. Classification Algorithms

• Overview of classification tasks
• Logistic Regression
  • Sigmoid function
  • Decision boundary
• Decision Trees
  • Tree construction
  • Pruning
• Support Vector Machines (SVM)
  • Concept of margin and hyperplane
  • Kernel functions
• k-Nearest Neighbors (k-NN)
  • Distance metrics
  • Choosing k
• Model training and evaluation
  • Accuracy, Precision, Recall, F1-score

4. Clustering Algorithms

• Concept of clustering and unsupervised learning
• K-means Clustering
  • Algorithm steps
  • Choosing number of clusters (k)
• Hierarchical Clustering
  • Agglomerative and divisive approaches
  • Dendrogram interpretation
• DBSCAN
  • Density-based clustering
  • Handling noise/outliers
• Evaluating clustering results
  • Silhouette score
  • Davies-Bouldin index

5. Neural Networks

• Structure of an artificial neural network
  • Neurons, layers (input, hidden, output)
• Activation functions
  • Sigmoid, ReLU, Tanh
• Forward propagation
• Backpropagation algorithm
• Optimization techniques
  • Gradient descent
  • Learning rate
• Introduction to deep learning
  • Deep neural networks
  • Convolutional and recurrent networks overview

6. Natural Language Processing (NLP)

• Overview of NLP tasks
• Text preprocessing
  • Tokenization
  • Stop word removal
  • Stemming and lemmatization
• Sentiment analysis
• Named entity recognition (NER)
• Text generation techniques

7. Computer Vision

• Introduction to computer vision
• Image classification
• Object detection
• Image segmentation
• Convolutional Neural Networks (CNNs)
  • Convolution operation
  • Pooling layers
  • CNN architectures overview

8. Reinforcement Learning

• Concept and motivation
• Reinforcement learning framework
  • Agent, environment, states, actions, rewards
• Markov decision processes (MDP)
• Key algorithms
  • Q-learning
  • Policy gradients (brief overview)

9. Model Evaluation and Hyperparameter Tuning

• Evaluation techniques
  • Cross-validation
  • Confusion matrix
  • ROC curves and AUC
• Hyperparameter tuning
  • Grid search
  • Random search
  • Bayesian optimization (brief introduction)
• Overfitting and underfitting
• Model selection strategies