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.
SUMMARY
EDUCATION
University of California San Diego
- Major: Computer Science and Engineering; GPA: 3.84
- In-depth learning of Python, Java, C, and C++ languages with 8 hours + weekly training experience on code training platforms.
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Past course experiences CSE and MATH
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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.
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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.
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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.
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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.
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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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SKILLS
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Machine Learning:Well-versed in manipulating multiple machine learning algorithms including KNN, L1&L2 regularization, and gradient optimization.
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AlgorithmsProficient in multiple searching algorithms including Dijkstra, A*, and quick sort.
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Reinforcement Learningmastered in manipulating multiple deep learning algorithms including reinforcement learning, Q-learning, and Markov decision tree.
- capable of understanding SQL statements and processing data using SQL commands.