This repository contains solutions to assignments, including programming tasks, project updates, and final project reports. Each assignment's solution is organized in separate folders, and clear instructions are provided for submission.
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- Course Name: DSCI 599 - Optimization Techniques for Data Science
- Instructor: Prof. Satish Thittamaranahalli Ka
- Semester: Fall 2023
A 4-unit course designed to explore various optimization techniques drawn from algorithms, artificial intelligence, machine learning, and mathematics. The course aims to provide students with a comprehensive understanding of optimization algorithms and their applications in solving problems relevant to data science. Topics covered include linear programming, convex programming, machine learning paradigms (supervised, unsupervised, reinforcement learning), graph theory, heuristic search, constraint programming, and more.
Caution
Please note that this repository serves as a reference guide and should be utilized as a tool for learning and comprehension. It's paramount to refrain from engaging in any activities associated with plagiarism. Embrace the wealth of knowledge herein to enhance your understanding and augment your skill set in the field of machine learning algorithms.
| Assignment | Topic Covered |
|---|---|
| Homework 1 | Dynamic Programming, Local Search, K-Means Clustering, Expectation Maximization, and FastMAP |
| Homework 2 | Guassian Mixture Models (GMMs), Multivariate Guassian, Principal Component Analysis (PCA), Dimensionality Reduction, Perceptron Learning Algorithm and Constraint Satisfaction Problems (CSPs) |
| Homework 3 | k-consistency, Backtracking search, Look-Ahead, and Dynamic Variable/Value Ordering in CSPs, Simple Temporal Problems (STPs), and Bayesian Networks |
| Homework 4 | Dynamic Belief Networks (DBNs), Markov Models (MMs), Hidden Markov Models (HMMs), Weighted Constraint Satisfaction Problems (WCSPs), Graph Theory, Feed-Forward Neural Networks and Polynomial Identity Checking |
| --- | --- |
| Final Project | Exploring Adversrial learning strategies for Sequence Labeling Tasks |
- Kayvan Shah |
MS in Applied Data Science|USC
This repository is licensed under the BSD 5-Clause License. See the LICENSE file for details.
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