• Contact Info

Gates, Ami

Teaching Professor




  • data science, machine learning, neural networks, text mining, NLP, programming, R, Python, visualization, applied math, statistics


courses taught

  • CSCI 5622 - Machine Learning
    Primary Instructor - Fall 2022 / Spring 2023 / Fall 2023
    Trains students to build computer systems that learn from experience. Includes the three main subfields: supervised learning, reinforcement learning and unsupervised learning. Emphasizes practical and theoretical understanding of the most widely used algorithms (neural networks, decision trees, support vector machines, Q-learning). Covers connections to data mining and statistical modeling. A strong foundation in probability, statistics, multivariate calculus, and linear algebra is highly recommended.
  • CSCI 5922 - Neural Networks and Deep Learning
    Primary Instructor - Fall 2022 / Fall 2023
    Introduces modern approaches to machine learning using neural networks. Neural nets, popular in the early 1990s, have undergone a resurgence due to significant advances in computing power and the availability of very large data sets. Now rechristened 'deep learning', the field has produced state-of-the-art results in a range of artificial intelligence problems, including vision, speech and natural language processing.
  • DTSA 5841 - IBM Capstone Project
    Primary Instructor - Spring 2023 / Summer 2023 / Fall 2023
    The IBM Capstone Project course will allow you to apply the knowledge and skills from the MS-DS degree to a real-world data set provided by IBM. This project will allow you to work independently on a data set that will test your skills in acquiring, cleaning, modeling data, and analyzing a dataset using data mining and machine learning techniques. By the end of this course, you will have a project that you can add to your data science portfolio to show off to employers and demonstrate your data science expertise. This course uses the IBM dataset from the IBM Applied Data Science Capstone course, part of the IBM Data Science Professional Certificate, and provides additional instruction and assessments in order to apply this capstone as an elective for the MS-DS Coursera degree at the University of Colorado Boulder. Because CU is collaborating with IBM on this course, all students will have full access to the IBM Applied Data Science Capstone Course while taking DTSA 5841. It is strongly recommended that you take the Capstone Project course as one of your final courses in the program as you will work with real-world data sets that will use MS-DS core concepts. In order to be successful in this course, only students who have completed the following specializations should register for the Capstone Project: Data Science Foundations: Data Structures and Algorithms, Data Science Foundations: Statistical Inference, Data Mining Foundations and Practice and Machine Learning.
  • DTSC 5930 - Professional Internship
    Primary Instructor - Summer 2023
    This class provides a structure for DS graduate students to receive academic credit for internships with industry partners that have an academic component to them suitable for graduate-level work. Participation in the program will consist of an internship agreement between a student and an industry partner who will employ the student in a role that supports the academic goals of the internship. Instructor participation will include facilitation of mid-term and final assessments of student performance as well as support for any academic-related issues that may arise during the internship period. Recommended prerequisite: may be taken during any term following initial enrollment and participation in DS graduate programs. May be repeated up to 3 total credit hours.
  • INFO 5871 - Special Topics
    Primary Instructor - Spring 2023
    Topics will vary by semester.
  • STAT 5000 - Statistical Methods and Application I
    Primary Instructor - Fall 2022
    Introduces exploratory data analysis, probability theory, statistical inference, and data modeling. Topics include discrete and continuous probability distributions, expectation, laws of large numbers, central limit theorem, statistical parameter estimation, hypothesis testing, and regression analysis. Considerable emphasis on applications in the R programming language. Same as STAT 4000.