CS702 Machine Learning / Advanced Algorithms Analysis and Design

CS702: Machine Learning / Advanced Algorithms

CS702 is a graduate-level course that delves deep into the foundational models of Machine Learning (ML) and the advanced algorithmic techniques required to implement them efficiently. This course builds upon the foundations of introductory AI (CS607) and algorithms (CS502), providing both the theoretical understanding and practical application of how computer systems can learn from data to make predictions and decisions.

The course is broadly split into the major paradigms of machine learning. You will explore supervised learning, where the algorithm learns from labeled data; unsupervised learning, where the algorithm finds hidden patterns in unlabeled data; and reinforcement learning, where an agent learns by interacting with its environment. This course is mathematically intensive, relying on concepts from probability, statistics, and linear algebra, but is firmly rooted in practical application.

Key Topics Covered:

• Supervised Learning:
  • Regression: Predicting a continuous value (e.g., house price). Includes Linear Regression and Polynomial Regression.
  • Classification: Predicting a discrete label (e.g., spam/not spam). Includes Logistic Regression, k-Nearest Neighbors (k-NN), Support Vector Machines (SVMs), Decision Trees, and Random Forests.
• Unsupervised Learning:
  • Clustering: Grouping similar data points together (e.g., K-Means Clustering, Hierarchical Clustering).
  • Dimensionality Reduction: Compressing data by finding its most important features (e.g., Principal Component Analysis (PCA)).
• Model Evaluation and Validation: The "science" of ML. How to properly test your model and avoid overfitting. This includes train/test splits, cross-validation, and metrics (e.g., accuracy, precision, recall, F1-score, confusion matrix).
• Introduction to Neural Networks: The building blocks of Deep Learning. Understanding the perceptron, multi-layer perceptrons (MLPs), and the backpropagation algorithm.
• Advanced Algorithm Analysis: A deeper dive into algorithm design, potentially covering randomized algorithms, approximation algorithms, and more complex data structures used to optimize ML models.

Course Objectives:

1. Gain a deep theoretical and practical understanding of the major machine learning models.
2. Implement and apply supervised and unsupervised learning algorithms to real-world datasets.
3. Master the techniques for training, validating, and evaluating machine learning models to prevent overfitting.
4. Understand the mathematical foundations of ML, including key concepts from statistics and linear algebra.
5. Build a foundation for advanced study in deep learning, natural language processing, and computer vision.

CS702 is an essential course for anyone aspiring to be a Data Scientist, Machine Learning Engineer, or AI Researcher. It provides the core toolkit for one of the most transformative technologies in computer science today.