Lyyoness / CS110-Solving-Problems-with-Algorithms
Hashing, searching, sorting, tree algorithms, dynamic programming, greedy algorithms, divide and conquer, random number generation, and randomized algorithms are examples of algorithms students learn to exploit to solve problems ranging from logistics and route optimization to DNA sequencing.
☆9Updated 6 years ago
Related projects: ⓘ
- Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference☆26Updated 4 years ago
- ICDSS Machine Learning Workshop Series: Machine Learning APIs☆10Updated 6 years ago
- ☆14Updated last year
- Notes, problems, simulations developed while following the MIT Open Courseware class "Probability Systems Analysis☆26Updated last year
- Learning how to apply advanced decision techniques such as real options, Monte Carlo simulation, network concepts from graph theory, prob…☆30Updated 5 years ago
- Course materials for the ORC's 2017 IAP course, "Computing in Optimization and Statistics"☆19Updated 6 years ago
- Numerical computing labs☆10Updated 9 years ago
- Relational NLP: Convert text into relational facts.☆9Updated 4 years ago
- The Path of the PyData Ninja☆16Updated 9 years ago
- Work for Mastering Large Datasets with Python☆18Updated last year
- Companion code for my PyData talk: "Introduction to Probabilistic Programming with PyMC3"☆13Updated 5 years ago
- Libraries for Scientific Computing☆10Updated 5 years ago
- Python data analysis course for 2017 NGCM Summer Academy☆19Updated 7 years ago
- PyData San Luis 2017 Tutorial: An Introduction to Gaussian Processes in PyMC3☆15Updated 6 years ago
- A tutorial demonstrating how to implement deep learning models for time series forecasting☆12Updated 4 years ago
- Classification of reserve risk with chain-ladder☆11Updated 5 years ago
- ☆13Updated 4 years ago
- My work on UCSD CSE 250B Principles of Artificial Intelligence: Learning Algorithms☆13Updated 5 years ago
- The Thalesians' LaTeX library☆11Updated 7 months ago
- Python notebooks for demonstrating various ideas, APIs, libraries.☆32Updated 7 years ago
- Solutions to machine learning HW from bloomberg ml course☆12Updated 5 years ago
- A selection of business datasets☆17Updated 5 years ago
- The 2020 Version of the Deep Learning Course☆8Updated 4 years ago
- Quantum Black Hackathon organised by Analytics Vidya☆12Updated 5 years ago
- Modelling Connectedness of Firms in Financial Markets with Heterogeneous Agents☆20Updated 5 years ago
- Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.☆20Updated 2 years ago
- Slides for my PyData NYC 2017 talk.☆14Updated 6 years ago
- A thorough, straightforward, un-intimidating introduction to Gaussian processes in NumPy.☆16Updated 6 years ago
- Course materials for Stat 243, fall 2016, at UC Berkeley☆9Updated 7 years ago
- Files for Python Talk☆24Updated 8 years ago