Angel Vilchis

Relevant Coursework

Intro to Graduate Algorithms

Design and analysis of algorithms such as: dynamic programming, divide and conquer, randomized algorithms, graph algorithms, linear programming, and NP-completeness.

Machine Learning

3-course ML series consisting of Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Deep Learning

Details of optimization methods (gradient descent and backpropagation), modules (linear, convolution, pooling layers, activation functions, etc), and architectures (CNNs, RNNs, etc.)

Educational Technology*

Deeply investigate tools, methods, and theories behind educational technology and deliver final project which includes presentation and publication.

Reinforcement Learning

Examination of algorithms for learning single-agent and multi-agent behaviorial policies. Topics include: Markov decision processes (MDPs), stochastic and repeated games, and partially observable MDPs.

Natural Language Processing*

Fundamental ideas as well as current state-of-the-art practice in developing NLP algorithms for common NLP problems: text classification, text generation, dialogue, language translation, and document retrieval.

Machine Learning for Trading

Application of Machine Learning to trading decisions using statistical approaches such as linear regression, Q-learning, KNN, and regression trees.

Data & Visual Analytics*

Introduction to broad range of techniques and tools for analyzing and visualizing data at scale to perform effective analysis.

AI, Ethics, & Society*

Examination of various techniques used to counterbalance potential abuse and misuse of learning from big data with focus on effects of these technologies on individuals, organizations, and society.

Computer Vision*

Introduction to computer vision including: fundamentals of image formation, camera imaging geometry, feature detection and matching, multiview geometry, and classification.

"*": planned