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YIFEI WANG

Email: yifeiwang.working@gmail.com
Contact Info: (+1) (858)-539-6611
LinkedIn account: www.linkedin.com/in/YifeiWang2002
Address: 3737 Nobel Drive, Apartment 2316, San Diego, CA 92122, USA


SUMMARY

A computer science and engineering bachelor candidate, with 2 years of learning experience in data science and software engineering and proficiency in using Java, C, C++, Python, and SQL.

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EDUCATION

University of California San Diego

  1. Major: Computer Science and Engineering; GPA: 3.84
  2. In-depth learning of Python, Java, C, and C++ languages with 8 hours + weekly training experience on code training platforms.
  3. Past course experiences CSE and MATH
    • CSE150B: Intro to deep learning

      Implementation of A* searching algorithm, Markov decision tree, Monte Carlo simulation, and other deep learning methods. Few of my counterparts have experience in writing learning small games, including 2048 or blackjack, and I have an advantage over them by having these valuable experiences.

    • CSE100: Advanced Data Structures

      sophisticated description of multiple data structures in computer science, including tree, hash, and searching algorithms. Compared with my counterparts, I can proficiently utilize appropriate algorithms to achieve the same functionality in less time and with less complexity.

    • CSE151A: Introduction to Machine Learning

      Broad introduction to machine learning. The topics include some topics in supervised learning, such as k-nearest neighbor classifiers, decision trees, boosting, and perceptrons; and topics in unsupervised learning, such as k-means and hierarchical clustering. In addition to the actual algorithms, the course focuses on the principles behind the algorithms.

    • CSE158: Recommender Systems and Web Mining

      Current methods for data mining and predictive analytics. Emphasis is on studying real-world data sets, building working systems, and putting current ideas from machine learning research into practice.

    • CSE250A: Principles of Artificial Intelligence: Probabilistic Reasoning and Learning

      Methods based on probability theory for reasoning and learning under uncertainty. Content may include directed and undirected probabilistic graphical models, exact and approximate inference, latent variables, expectation-maximization, hidden Markov models, Markov decision processes, applications to vision, robotics, speech, and/or text.



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