Machine Learning With
Artificial Intelligence

Being a successful student requires understanding how you learn, identifying your values, and setting clear goals. It also means mastering time and financial management, developing strong note-taking and test-taking skills, collaborating effectively, and leveraging technology to enhance your efficiency. In this course, you will learn essential communication, writing, and presentation skills. Additionally, you will focus on financial literacy through the Enriched Academy program, designed specifically for Oxford College students, to help you confidently manage your finances.

This module lays the foundation for understanding the mathematical principles that are vital for data analytics. Students will enhance their logical thinking and problem-solving skills while exploring algebra and linear algebra. Key topics include operations on vectors and matrices, as well as statistical analysis, data mining, natural language processing, and computer vision. By the end of the course, students will be well-prepared to apply these mathematical tools to real-world data science algorithms.

Calculus is an essential tool in data science, providing a framework for optimization and data analysis. This module focuses on how calculus applies to key areas such as probability distributions, gradient descent, and neural networks. Students will develop the skills to analyze complex datasets and build robust models, giving them the foundation needed to extract valuable insights in data science.

This module introduces students to the key concepts of trigonometry, which is crucial for modeling periodic data. Trigonometric principles help in extracting meaningful features from data, essential for building effective machine learning models. Students will learn to apply these principles in data analysis to improve their understanding of periodic patterns in datasets.

Statistics provides the essential framework for understanding and evaluating machine learning models. This module explores the fundamentals of probability and statistical theory, giving students the knowledge needed to build and assess models in machine learning and artificial intelligence. Students will gain the skills to apply statistical techniques to analyze data effectively.

Artificial Intelligence (AI) is revolutionizing industries by improving efficiency and performance in various tasks. This module will introduce students to the basics of AI and its applications in machine learning. It will also discuss the ethical considerations surrounding AI, preparing students to understand both the potential and the responsibilities of deploying AI solutions in real-world scenarios.

Python’s popularity stems from its ease of use and flexibility, making it a key programming language in data science. This module will teach students the fundamentals of programming, focusing on Object-Oriented Programming (OOP) principles. Key topics include data abstraction, constructors, destructors, and polymorphism, which will help students build efficient and organized software for data analysis.

This module introduces the fundamental concepts of machine learning, allowing students to understand how computers learn from data. Students will explore a wide range of applications, such as automation, pattern recognition, and predictive analytics. By the end of the course, students will have a strong foundation in machine learning techniques and their impact on solving real-world problems.

In this module, students will focus on supervised Machine Learning techniques, where algorithms are trained on labeled datasets to make predictions and classifications. Topics covered include linear and logistic regression, which are essential for building predictive models. Students will also learn to use Python libraries like Pandas and scikit-learn for practical applications of supervised learning.

Unsupervised learning allows students to explore data without labeled examples, discovering hidden patterns and structures. This module covers techniques like clustering and dimensionality reduction. Students will gain the skills to apply unsupervised learning algorithms to solve problems such as anomaly detection and pattern discovery in datasets.

Neural networks are inspired by the human brain and are essential for tasks such as classification and pattern recognition. In this module, students will explore basic machine learning techniques using neural networks, focusing on activation functions and the structure of these networks. Practical skills will also be developed using popular frameworks like PyTorch and TensorFlow.

This introductory course in computer vision teaches students how to analyze and interpret visual data from images and videos. Students will learn about Convolutional Neural Networks (CNNs) and their use in image recognition and processing. The course will also cover pre-trained networks and transfer learning, providing students with the skills needed to apply computer vision techniques in various applications.

Natural Language Processing (NLP) enables computers to understand, interpret, and generate human language. This module will introduce students to the core concepts and applications of NLP, including sentiment analysis, text classification, and language generation. By the end of the course, students will understand how to use NLP to process and automate language-based tasks.

Large Language Models (LLMs) are advanced AI models that process and generate human-like text. In this module, students will learn how to work with LLMs to solve real-world problems. Topics will include data preparation, model scaling, and the risks involved in deploying LLMs. Students will gain a comprehensive understanding of LLM applications in AI.

In this hands-on project, students will apply their machine learning skills to analyze syslog messages in network systems. Over 40 hours, students will synthesize their learning to develop meaningful insights, using machine learning techniques to improve network operations and performance analysis. This project provides practical experience in applying ML to real-world data.

This placement module provides students within the Machine Learning in AI program with the opportunity to apply theoretical knowledge to real-world problems while gaining hands-on experience in the field. Students will work with popular libraries and cloud-based platforms, implement supervised and unsupervised learning models, and engage in data wrangling, preprocessing, and feature engineering. This practical experience will enhance their problem-solving skills and prepare them for careers in AI and machine learning.