Unit I: Regression and Classification Models in Supervised Learning
9 Introduction to Supervised Learning
- Basic framework of supervised machine learning
- Overview of regression and classification models
10 Regression Models
- Linear regression
- Multiple regression
- Polynomial regression
- Nonlinear regression
- Quantile regression
11 Advanced Regression Techniques
- Lasso regression
- Ridge regression
- Stepwise regression
Unit II: Advanced Machine Learning Methods and Unsupervised Learning
13 Advanced Techniques in Supervised Learning
- Linear discriminant analysis
- Principal component analysis (PCA)
- Factor analysis
- Support vector machines (SVM)
- Naïve Bayes classifier
- Nearest neighbors
- Decision trees
- Random forest
- Ensemble methods
14 Model Validation and Improvement
- K-fold cross-validation
- X Gradient Boosting
15 Introduction to Unsupervised Learning
- Basic framework of unsupervised machine learning
- Concepts of clustering
16 Clustering Techniques
- K-means clustering
- C-means clustering
- Hierarchical clustering
17 Advanced Topics in Unsupervised Learning
- Hidden Markov models
- Forecasting models:
- AR (Auto-Regressive)
- MA (Moving Average)
- ARMA (Auto-Regressive Moving Average)
- ARIMA (Auto-Regressive Integrated Moving Average)
Unit III: Deep Learning and Applications in Agribusiness
18 Introduction to Deep Learning
- Basic framework of neural networks
- Types of neural networks
19 Advanced Neural Network Techniques
- Feedforward neural networks
- Backpropagation
- Recurrent neural networks (RNN)
- Convolutional neural networks (CNN)
- Reinforcement neural networks
- Concurrent neural networks
20 Deep Learning Applications
- Computer vision
- Object detection and localization
21 Optimization Techniques
- Gradient descent optimization for loss function
- Regularization methods: L1 and L2
22 IoT and Agribusiness Applications
- Introduction to IoT
- Applications of deep learning in agribusiness
23 Practical Applications Using R Studio
- Illustrations from agribusiness domains
- Practical exercises in R Studio