Block II. Machine and Deep Learning

Block II moves from supervised learning through advanced machine-learning methods, unsupervised learning, and deep learning, closing with IoT and hands-on agribusiness applications in R.

Unit I. Regression and Classification Models in Supervised Learning

  1. Introduction to Supervised Learning
  2. Regression Models
  3. Advanced Regression Techniques
  4. Classification Models

Unit II. Advanced Machine Learning Methods and Unsupervised Learning

  1. Advanced Techniques in Supervised Learning
  2. Model Validation and Improvement
  3. Introduction to Unsupervised Learning
  4. Clustering Techniques
  5. Advanced Topics in Unsupervised Learning

Unit III. Deep Learning and Applications in Agribusiness

  1. Introduction to Deep Learning
  2. Advanced Neural Network Techniques
  3. Deep Learning Applications
  4. Optimization Techniques
  5. IoT and Agribusiness Applications
  6. Practical Applications Using R Studio