These are the classes I’m taking or have completed in seeking my Master’s degree in Computer Science from CU-Boulder.

Spring 2019 Classes

  • CSCI 5423: Bio-Inspried Multi-Agent Systems
    • Professor: Dr. Orit Peleg
    • Description: “Collective dynamics allow super-organisms to function in ways that a single organism cannot, by virtue of their emergent size, shape, physiology and behavior. Examples include self-assemblage of cells into a multicellular organism, insect colonies that forage vast areas and construct complex nests, and bird flocks that migrate with high efficiency and evade predators. How can we use this knowledge to engineer robust collective behavior from the cooperation of a vast number of agents with simple capabilities? This class will survey bio-inspired approaches to understanding and designing collective intelligence, covering (i) swarm intelligence: social insects and animal groups, with applications to networking and robotics, (ii) cellular computing: including cellular automata/amorphous computing, and applications like self-assembling robots and programmable materials, and (iii) evolutionary computation and its application to optimization and design.”
  • CSCI 5446 (auditing only): Chaotic Dynamics
    • Professor: Dr. Liz Bradley
    • Description: “This course explores dynamical systems and the various ways to use a computer to investigate their behavior. It covers the standard computational and analytical tools used in nonlinear dynamics, together with their underlying theory, and concludes with a brief review of leading-edge chaos research. Examples of important topics and techniques are: state-space representation, interpretation, and surfaces of section; steady-state solutions and limit sets; numerical integration; time and frequency domain analysis; bifurcation diagrams; fractals, fractal dimension, and the link between fractals and chaos; etc. Students construct their own computational tools and use them to explore interesting chaotic systems, ranging from mechanical pendulums to biological populations to electronic circuits.”
  • INFO 5900: Master’s Level Independent Study
    • Professor: Dr. Chenhao Tan
    • Description: Studying counterfactual machine explanations

Fall 2018 Classes

  • CSCI 5352: Network Analysis and Modeling
    • Professor: Dr. Daniel Larremore
    • Description: “Network science is a thriving and increasingly important cross-disciplinary domain that focuses on the representation, analysis, and modeling of complex social, biological and technological systems as networks or graphs. Modern data sets often include some kind of network. Nodes can have locations, directions, memory, demographic characteristics, content, and preferences. Edges can have lengths, directions, capacities, costs, durations, and types. And, these variables and the network structure itself can vary, with edges and nodes appearing, disappearing and changing their characteristics over time. Capturing, modeling and understanding networks and rich data requires understanding both the mathematics of networks and the computational tools for identifying and explaining the patterns they contain. This graduate-level course will examine modern techniques for analyzing and modeling the structure and dynamics of complex networks. The focus will be on statistical algorithms and methods, and both lectures and assignments will emphasize model interpretability and understanding the processes that generate real data. Applications will be drawn from computational biology and computational social science.”
  • CSCI 5253: Datacenter Scale Computing
    • Professor: Dr. Eric Rozner
    • Description: “This course covers the primary problem solving strategies, methods and tools needed for data-intensive programs using large collections of computers typically called “warehouse scale” or “data-center scale” computers. CSCI 5253 examines methods and algorithms for processing data-intensive applications, methods for deploying and managing large collections of computers in an on-demand infrastructure and issues of large-scale computer system design. Students will be expected to implement solutions on Amazon’s cloud.”
  • INFO 5900: Master’s Level Independent Study
  • Fall 2018 Work: TA For Data Structures (CSCI 2270)

Spring 2018 Classes

  • CSCI 7000: CS Topics: Human-Centered Machine Learning
    • Professor: Dr. Chenhao Tan
    • Description: “Machine learning research has been focusing on improving the capability of machines, which sometimes even outperforms humans. In this course, we will center around humans and explore how we can use machine learning to improve human performance in various scenarios. We will cover topics such as interpretable machine learning, machine teaching, cognitive bias, education, accessibility, etc. This is a research focused class. Students are expected to discuss related papers, work on research proposals, and finish a final project on related topics.”
  • CSCI 5822: Probabilistic Models
    • Professor: Dr. Michael Mozer
    • Description: “In artificial intelligence and cognitive science, the formal language of probabilistic reasoning and statistical inference have proven useful to model intelligence. From a probabilistic perspective, knowledge is represented as degrees of belief, observations provide evidence for updating one’s beliefs, and learning allows the mind to tune itself to statistics of the environment in which it operates. One virtue of probabilistic models is that they straddle the gap between cognitive science, artificial intelligence, and machine learning. The same methodology is useful for both understanding the brain and building intelligent computer systems.”
  • INFO 5602: Information Visualization
    • Professor: Dr. Danielle Szafir
    • Description: “Data is everywhere. Charts, graphs, and other types of information visualizations help people to make sense of this data. This course explores the design, development, and evaluation of these information visualizations. By combining aspects of design, computer graphics, HCI, and data science, you will gain hands-on experience with creating visualizations, using exploratory tools, and architecting data narratives. Topics include interactive systems, user-centered and graphic design, graphical perception and cognition, data storytelling, and insight building. Throughout this course, you will work directly with stakeholders to analyze data from a variety of domains and applications.”

Fall 2017 Classes

  • CSCI 5622: Machine Learning
    • Professor: Dr. Chenhao Tan
    • Description: “Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling.”
  • CSCI 5454: Design and Analysis of Algorithms
    • Professor: Dr. Ashutosh Trivedi
    • Description: “Techniques for algorithm design, analysis of correctness and efficiency; divide and conquer, dynamic programming, probabilistic methods, advanced data structures, graph algorithms, etc. Lower bounds, NP-completeness, intractability. Recommended prerequisite: CSCI 2270 or equivalent.”
  • CSCI 5832: Natural Language Processing
    • Professor: Dr. James Martin
    • Description: “Explores the field of natural language processing as it is concerned with the theoretical and practical issues that arise in getting computers to perform useful and interesting tasks with natural language. Covers the problems of understanding complex language phenomena and building practical programs.”