1. Project: Check the FutureFit Project which you can find in my Repository

1.1 FutureFit

Figure 1:Project

2. Lab Assignments:

assignment1: Dicision Tree & Random Forest
assignment2: Gradiant Boosting
assignment3: Regression
assignment4: K-means Clustering

3. Theory Lecture Outline

• Week 1 Introduction to Machine Learning, Random Forest
• Week 2 Gradient Boosting: Regression, Classification
• Week 3 Linear Prediction: Linear Regression (Lasso, Elastic Net), Logistic Regression
• Week 4 Support Vector Machines (SVC: Linear and Kernel-trick)
• Week 5. Probabilistic ML Models: NB, GNB, GMM
• Week 6. Unsupervised Learning: Clustering (K-means, Fuzzy C-Means), DBSCAN
• Week 7. Mid-Week
• Week 8. Data Dimensionality Reduction ( LDA, and tSNE)
• Week 9. Model Assessment and Performance Metrics: Classification, Clustering and Regression
• Week 10. Artificial Neural Networks: Perceptron, MLPs, and Backpropagation
• Week 11. Deep Neural Networks: CNN, LSTM
• Week 12. Final Exam