Collaborative Computer Science & coding lessons with creativity

The class covered Thevenin's and Norton's theorems for simplifying electrical circuits. The student practiced applying these theorems to calculate equivalent voltage/current sources and resistances in given circuits, with further concepts like AC circuit analysis and phasors mentioned for future sessions.

The session covered mesh analysis, including super mesh analysis for circuits with current sources bridging loops. The tutor and student also reviewed the superposition theorem, practicing its application to calculate currents and voltages by considering each source independently and then summing the results. The student was assigned practice problems for both mesh and superposition theorems.

The Tutor and Student reviewed advanced regression models, focusing on XGBoost as a superior alternative to Decision Trees and Random Forests due to its gradient boosting approach. They implemented XGBoost using Python, discussed key parameters and ensemble methods (bagging vs. boosting), and explored visualization techniques for model importance and tree structure. The next steps will involve moving to classification models.

The student and tutor practiced applying nodal analysis and the supernode concept to solve complex electrical circuits. They worked through several example problems, focusing on identifying nodes, formulating equations, and solving for unknown voltages and currents. The next topic planned is supermesh analysis.

The student and tutor reviewed advanced circuit analysis techniques, focusing on super node analysis for nodal analysis and briefly touching upon super mesh analysis for mesh analysis. They worked through example problems to solidify the understanding of applying these methods to circuits with voltage sources between non-reference nodes.

The class focused on machine learning concepts, specifically decision trees and random forests. The tutor explained how decision trees are built using MSE to split data and discussed their limitations, leading into the introduction of random forests as an ensemble method to improve accuracy. Future topics will include other regression and classification models.