Code for homeworks and final project in which I implemented AI algorithms. The code is located in:

• hw1/: depth-first, breadth-first, uniform-cost and A* search, modified search.py and searchAgents.py;
• hw2/: minimax, expectimax and alpha-beta pruning, modified game.py, multiAgents.py, pacman.py and util.py;
• hw3/: Markov decision processes, value iteration and Q-learning, modified analysis.py, qLearningAgents.py, and valueIterationAgents.py;
• hw4/: Bayes nets, modified bustersAgents.py and inference.py; and
• project/: naive Bayes, RELIEF and chi-square scores, main code located under directory defender/overwatch/ in models.py, util.py, management/commands/loadnsdump.py and management/commands/bayes.py.

Code for cumulative homeworks where I and two partners implemented portions of a simple operating system. The code we modified is located in four directories under the directory src/:

• threads/: threads and scheduling, modified thread.c and thread.h;
• userprog/: user programs and system calls, modified process.c, process.h and syscall.c;
• vm/: virtual memory and paging, modified frame.c, frame.h, page.c, page.h, swap.c and swap.h; and
• filesys/: file system, modified cache.c, cache.h, directory.c, directory.h, filesys.c, filesys.h, free-map.c, free-map.h, inode.c and inode.h.

Code for homeworks in which I implemented NLP algorithms. The code is located in:

• hw1/: finite state machines;
• hw2/: context-free grammars; and
• hw3/: context-free parsing and machine translation.

Code for homeworks in which I implemented machine learning algorithms. The code is located in:

• hw1/: linear regression (housing_regression.py) and learning trees (housing_tree.py);
• hw2/: neural networks (neural.py), perceptron (perceptron.py) and gradient descent (regress.py);
• hw3/: naive Bayes (bayes.py), expectation maximization (em.py) and Gaussian discriminant analysis (gda.py);
• hw4/: active learning (al.py), adaptive boosting (adaboost.py) and gradient boosting (gradient_boosting.py);
• hw5/: error-correcting output codes (ecoc.py), Haar-like features (haar.py) and principle component analysis (pca.py);
• hw6/: support vector machines (svm.py); and
• hw7/: k-nearest neighbors (knn.py) and RELIEF (relief.py).

Code homeworks in which I implemented data mining techniques. The code is located in:

• hw1/: linear regression (regress.py);
• hw2/: k-nearest neighbors (knn.py);
• hw3/: k-means (kmeans.py); and
• hw4/: feature selection with the filter (filter.py) and wrapper (wrapper.py) methods.

Code homeworks in which I implemented programs for analyzing large data sets in parallel. The code is located in:

• hw1/: analyzing atmospheric data using Java threads;
• hw2/: analyzing atmospheric data using MapReduce;
• hw3/: implementing the PageRank algorithm using MapReduce;
• hw4/: implementing the PageRank algorithm using Spark; and
• hw5/: implementing the PageRank algorithm in Spark using matrix multiplication.