Gaurav's 2nd šŸ§ 

    University Of Pennsylvania

    Courses that interest me

    • Machine-learning courses:

      • CIS 5200 Machine Learning
      • CIS 5190 Introduction to Machine Learning
      • CIS 5210 Artificial Intelligence

    • CISĀ 4190 Applied Machine Learning

      Machine learning has been essential to the success of many recent technologies, including autonomous vehicles, search engines, genomics, automated medical diagnosis, image recognition, and social network analysis, among many others. This course will introduce the fundamental concepts and algorithms that enable computers to learn from experience, with an emphasis on their practical application to real problems. This course will introduce supervised learning (decision trees, logistic regression, support vector machines, Bayesian methods, neural networks and deep learning), unsupervised learning (clustering, dimensionality reduction), and reinforcement learning. Additionally, the course will discuss evaluation methodology and recent applications of machine learning, including large scale learning for big data and network analysis.

      Fall or Spring

      Mutually Exclusive:Ā CISĀ 5190

      Prerequisite:Ā CISĀ 1210

      1 Course Unit

    • CISĀ 4210 Artificial Intelligence

      This course investigates algorithms to implement resource-limited knowledge-based agents which sense and act in the world. Topics include, search, machine learning, probabilistic reasoning, natural language processing, knowledge representation and logic. After a brief introduction to the language, programming assignments will be in Python.

      Fall

      Mutually Exclusive:Ā CISĀ 5210

      Prerequisite:Ā CISĀ 1210Ā AND (ESEĀ 3010Ā ORĀ STATĀ 4300)

      1 Course Unit

    • CISĀ 5050 Software Systems

      This course provides an introduction to fundamental concepts of distributed systems, and the design principles for building large scale computational systems. Topics covered include communication, concurrency, programming paradigms, naming, managing shared state, caching, synchronization, reaching agreement, fault tolerance, security, middleware, and distributed applications. This course is appropriate as an upper-level undergraduate CIS elective. Prerequisite: Undergraduate-level knowledge of Operating Systems and Networking, programming experience. Prerequisite: Undergraduate-level knowledge of Operating Systems and Networking.

      Fall or Spring

      Prerequisite:Ā CITĀ 5940

      1 Course Unit

    • CISĀ 5220 Deep Learning for Data Science

      Deep learning techniques now touch on data systems of all varieties. Sometimes, deep learning is a product; sometimes, deep learning optimizes a pipeline; sometimes, deep learning provides critical insights; sometimes, deep learning sheds light on neuroscience or vice versa. The purpose of this course is to deconstruct the hype by teaching deep learning theories, models, skills, and applications that are useful for applications.

      Fall

      1 Course Unit

    • CISĀ 5730 Software Engineering

      Writing a "program" is easy. Developing a "software product", however, introduces numerous challenges that make it a much more difficult task. This course will look at how professional software engineers address those challenges, by investigating best practices from industry and emerging trends in software engineering research. Topics will focus on software maintenance issues, including: test case generation and test suite adequacy; code analysis; verification and model checking; debugging and fault localization; refactoring and regression testing; and software design and quality. Prerequisite: Proficiency in Java.

      Fall

      Prerequisite:Ā CITĀ 5940Ā ORĀ CISĀ 3500

      1 Course Unit

    • CISĀ 6200 Advanced Topics in Machine Learning

      This course covers a variety of advanced topics in machine learning, such as the following: statistical learning theory (statistical consistency properties of surrogate loss minimizing algorithms); approximate inference in probabilistic graphical models (variational inference methods and sampling-based inference methods); structured prediction (algorithms and theory for supervised learning problems involving complex/structured labels); and online learning in complex/structured domains. The precise topics covered may vary from year to year based on student interest and developments in the field.

      Spring

      Prerequisite:Ā CISĀ 5200

      1 Course Unit

    CIS Research Areas