Comp70091 Introduction To Machine Learning Department Of Computing

10-301 + 10-601, Fall 2025 School of Computer Science Carnegie Mellon University Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as decision tree learning, neural networks, statistical learning methods, unsupervised learning and reinforcement learning. The course covers theoretical concepts such as inductive bias, the PAC learning framework, Bayesian learning methods, and Occam’s Razor. Programming assignments include hands-on experiments with various learning algorithms.

This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning. 10-301 and 10-601 are identical. Undergraduates must register for 10-301 and graduate students must register for 10-601. Learning Outcomes: By the end of the course, students should be able to: For more details about topics covered, see the Schedule page. Upon completing this course, students will be able to do the following:

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement … This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences.

This course is part of the Open Learning Library, which is free to use. You have the option to sign up and enroll in the course if you want to track your progress, or you can view and use all the materials without enrolling. Welcome to the start of your Introduction to Machine Learning journey! We hope you will have a lot of fun and learn a lot! Meet your instructors in this introduction video! This introductory machine learning course will give an overview of many models and algorithms used in modern machine learning, including linear models, multi-layer neural networks, support vector machines, density estimation methods, bayesian belief networks,...

The course will give the student the basic ideas and intuition behind these methods, as well as, a more formal understanding of how and why they work. Through homework assignments students will have an opportunity to experiment with many machine learning techniques and apply them to various real-world datasets. Academic Career: Undergraduate Course Component: Lecture Grade Component: LG/SNC Elective Basis Course Requirements: PREQ: CS 1501 or COE 1501 and (STAT 1000 or 1100 or 1151 or ENGR 0020) (Min Grade 'C' or Transfer... Information Science Building, Fifth Floor 135 North Bellefield Avenue Pittsburgh, PA 15260 Analyzing large data sets (“Big Data”), is an increasingly important skill set. One of the disciplines being relied upon for such analysis is machine learning.

In this course, we will approach machine learning from a practitioner’s perspective. We will examine the issues that impact our ability to learn good models (e.g., inductive bias, the curse of dimensionality, the bias-variance dilemma, and no free lunch). We will then examine a variety of approaches to learning models, covering the spectrum from unsupervised to supervised learning, as well as parametric versus non-parametric methods. Students will explore and implement several learning algorithms, including logistic regression, nearest neighbor, decision trees, and feed-forward neural networks, and will incorporate strategies for addressing the issues impacting performance (e.g., regularization, clustering, and dimensionality... In addition, students will engage in online discussions, focusing on the key questions in developing learning systems. At the end of this course, students will be able to implement and apply a variety of machine learning methods to real-world problems, as well as be able to assess the performance of these...

Prerequisite(s): EN.605.202 – Data Structures or equivalent. EN.605.202 – Data Structures or equivalent, EN.605.621 – Foundations of Algorithms or EN.685.621 – Algorithms for Data Science or 705.621 – Introduction to Algorithms Lectures: Monday and Wednesday, 10:30AM to 11:50AM Instructor: Geoffrey J. Gordon and Alex Smola TAs: Carlton Downey, Ahmed Hefny, Dougal Sutherland, Leila Wehbe, and Jing Xiang

Grading Policy: Homework (33%), Midterm (33%), Project (34%). 091713 Moved site from ixwebhosting to a dedicated host on Amazon EC2. Performance should be much better now. Machine learning studies the question: “how can we build adaptive algorithms that automatically improve their performance on a given task as they acquire more experience?” This can cover a wide array of technologies depending... Through this framing, we might view classical statistics problems, like estimating the likelihood that a coin lands on heads as an ML problem: the task is to produce an estimate, and the experience would... But ML can also include robotics challenges, where the experience is acquired dynamically as our artificial agent interacts with the real world.

Other grand challenges in machine learning relate to personalized medicine, materials discovery, and most recently generating media artifacts like images and text. This course is designed to give PhD students a solid foundation in the methods, mathematics, and algorithms of modern machine learning. Students entering the class with a pre-existing working knowledge of probability, statistics and algorithms will be at an advantage, but the class has been designed so that anyone with a strong mathematical and computer... If you are interested in this topic, but are not a PhD student, or are a PhD student not specializing in machine learning, you might consider the master's level course on machine learning, 10-601. This class may be appropriate for MS and undergraduate students who are interested in the theory and algorithms behind machine learning. By the end of the course, students should be able to:

Students entering the class are expected to have pre-existing working knowledge in the following areas: Significant experience programming in a general programming language. Specifically, you need to have written from scratch programs consisting of several hundred lines of code. For undergraduate students, this will be satisfied for example by having passed 15-122 (Principles of Imperative Computation) with a grade of ‘C’ or higher, or comparable courses or experience elsewhere.

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