Recursion (Hard), OS scheduling, Ensembles: Boosting & XGBoost

CampusX: Adaptive Boosting (AdaBoost) algorithm, sample weights update rule, decision stumps, stage-wise additive modeling + StatQuest: "AdaBoost, Clearly Explained"

HRRN, Multilevel Feedback Queue Scheduling, Multicore Scheduling, Cache locality & NUMA + Kernel Structures (Monolithic vs Microkernel vs Hybrid) + Quiz

CampusX: Gradient Boosting Machine (GBM) intuition, pseudo-residuals, learning rate (shrinkage), loss functions + StatQuest: "Gradient Boost," Parts 1–4

User-level vs Kernel-level threads, POSIX threads, Thread pools, Race conditions, Critical section requirements (Mutual exclusion, Progress, Bounded waiting) + Quiz

CampusX: Extreme Gradient Boosting (XGBoost), regularized objective, second-order Taylor expansion, tree pruning, handling missing values + StatQuest: "XGBoost," Parts 1–4 + hands-on Python xgboost

Implement simplified AdaBoost from scratch; understand pseudo-residuals in Gradient Boosting; tune learning rate.

Quant M12 Average (Basic & Advance) + Verbal: Mixed Practice

Kaggle Learn "Intermediate Machine Learning"; hyperparameter tuning with Optuna/GridSearchCV on your Kaggle competition submission.

Logical: Coding and Decoding (Basic & Advance) + Dekker's Algorithm, Peterson's Algorithm, Bakery Algorithm, Hardware Synchronization (Test-and-Set, Compare-and-Swap), Mutexes, Counting & Binary Semaphores