- Understand fundamental concepts of supervised learning techniques.
- Learn to implement regression and classification models effectively.
- Explore unsupervised learning methods for data clustering.
- Grasp the basics of neural networks in deep learning contexts.
- Develop skills to evaluate model performance accurately.
- Acquire knowledge of advanced deep learning architectures.
- Apply machine learning solutions to real-world data problems.
UNIT 1. SUPERVISED LEARNING (I) 1. Introduction 2. Simple, multiple and logistic linear regression (I) 3. Simple, multiple and logistic linear regression (II) 4. Support vector machines (SVM) 5. Decision trees UNIT 2. SUPERVISED LEARNING (II) 1. KNN (k-nearest neighbors) 2. Naive Bayes 3. Evaluation of supervised models 4. Example exercise 5. Proposed exercise UNIT 3. UNSUPERVISED LEARNING 1. Introduction to clustering: purconsider and metrics 2. K-means clustering 3. Hierarchical clustering, other techniques and examples 4. Principal component analysis (PCA) 5. PCA example exercise UNIT 4. DEEP LEARNING 1. Artificial Neural Networks (ANN) (I) 2. Artificial Neural Networks (ANN) (II) 3. Artificial Neural Networks (ANN) (III) 4. Example exercise 5. Proposed exercise