Courses

TTIC Core ML Course Sequence

CS 35300 Mathematical Foundations of Machine Learning
This course is an introduction to the mathematical foundations of machine learning that focuses on matrix methods and features real-world applications ranging from classifi cation and clustering to denoising and data analysis. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Matlab, Python, Julia, R). Appropriate for graduate students or advanced undergraduates.

 

TTIC 31020 Introduction to Statistical Machine Learning
This is a graduate-level CS course with the main target audience being TTIC PhD students (for which it is required) and other CS, statistics, CAM and math PhD students with an interest in machine learning.  PhD students in other departments, as well as masters students and undergraduates, with sufficient mathematical and programming background, are also welcome to take the course, at the instructor’s permission.  The course relies on a good math background, as can be expected from a CS PhD student.  It provides a systematic introduction to machine learning and survey of a wide range of approaches and techniques.  The course this coming year will probably a bit heavier, covering slightly more material, compared to the past 2-3 years.
TTIC 31120 Statistical and Computational Learning Theory
This is a rigorous mathematical course providing an analytic view of machine learning.  It requires a high degree of mathematical maturity, typical of mathematically-oriented CS and statistics PhD students or math graduates.  It is typically taken by students who have already taken TTIC 31020 or a similar course, but is sometimes appropriate as a first machine learning course for very mathematical students that prefer understanding a topic through definitions and theorems rather then examples and applications.
TTIC 31230 Fundamentals of Deep Learning
Focuses specifically on deep learning and emphasizes theoretical and intuitive understanding.  Requires TTIC 31020 as a prerequisite, and relies on a similar or slightly higher mathematical preparation.

UC Core Course Sequence for ML PhD Students

CS 35300 Mathematical Foundations of Machine Learning
This course is an introduction to the mathematical foundations of machine learning that focuses on matrix methods and features real-world applications ranging from classifi cation and clustering to denoising and data analysis. Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Students are expected to have taken calculus and have exposure to numerical computing (e.g. Matlab, Python, Julia, R). Appropriate for graduate students or advanced undergraduates.

 

CS 35400 Machine Learning
TTIC 31020 Introduction to Statistical Machine Learning
This is a graduate-level CS course with the main target audience being TTIC PhD students (for which it is required) and other CS, statistics, CAM and math PhD students with an interest in machine learning.  PhD students in other departments, as well as masters students and undergraduates, with sufficient mathematical and programming background, are also welcome to take the course, at the instructor’s permission.  The course relies on a good math background, as can be expected from a CS PhD student.  It provides a systematic introduction to machine learning and survey of a wide range of approaches and techniques.  The course this coming year will probably a bit heavier, covering slightly more material, compared to the past 2-3 years.

UC Recommended Courses for Students in Other Disciplines

Computer Vision Focus
  • CMSC 25040 – Introduction to Computer Vision
  • TTIC 31040 – Introduction to Computer Vision
  • CMSC 35300 – Mathematical Foundations of Machine Learning
Natural Language Processing (NLP) Focus
  • CMSC 25700 – Natural Language Processing
  • TTIC 31190 – Natural Language Processing
  • TTIC 31230 – Fundamentals of Deep Learning
Computational Statistics Sequence (For students in Stats/Applied Fields)
  • STAT 30900 / CMSC 3781 – Mathematical Computation I (Matrix Computation) (Fall)
  • STAT 31015 / CMSC 37811 – Mathematical Computation II (Convex Optimization) (Winter)
  • STAT 37710 / CMSC 35400 – Machine Learning (Spring)

Background Preparation for ML

 

Mathematics & Optimization

Students without a strong background in math, optimization, or CS theory should consider the following foundational courses:

  • TTIC 31150 / CMSC 31150 – Mathematical Toolkit (Fall)
  • TTIC 31070 – Convex Optimization (Fall)
  • CMSC 37000 – Algorithms (Winter)
  • TTIC 31080 – Approximation Algorithms (Spring)

Machine Learning Themed Courses

Core ML Courses

Additional ML-related courses spanning core methodologies, inference, and advanced topics.

  • STAT 37601 / CMSC 25025 – Machine Learning and Large-Scale Data Analysis (Spring)
  • STAT 37400 – Nonparametric Inference (Fall)
  • STAT 41500-41600 – High-Dimensional Statistics (Autumn/Spring)
  • STAT 37500 – Pattern Recognition (Spring)
  • STAT 37750 – Compressed Sensing (Spring)
  • STAT 34000 – Gaussian Processes (Spring)
  • TTIC 31180 – Probabilistic Graphical Models (Spring)
  • TTIC 31120 – Statistical and Computational Learning Theory (Spring)
ML Application Courses

For students interested in specific real-world applications of ML.

  • Natural Language Processing: TTIC 31190 – Natural Language Processing (Winter)

  • Bioinformatics & Computational Biology: TTIC 31050 – Intro to Bioinformatics (Winter)

  • Computer Vision & Image Analysis: TTIC 31040 – Intro to Computer Vision (Winter)

  • Speech & Audio Processing: TTIC 31110 – Speech Technologies (Spring)

Additional Resources and Requirements 

Additional Course Information

The computational statistics sequence

Background from mathematics, optimization and CS theory

Machine learning themed courses

  • STAT 37601/CMSC 25025: Machine Learning and Large Scale Data Analysis (Lafferty) Spring.
  • STAT 37400: Nonparametric Inference (Lafferty) Fall.
  • STAT 41500-41600: High Dimensional Statistics. Autumn/Spring.
  • STAT 37500: Pattern Recognition (Amit) Spring.
  • STAT 37750: Compressed Sensing (Foygel-Barber) Spring.
  • STAT 34000: Gaussian Processes (Stein) Spring.
  • TTIC 31180: Probabilistic Graphical Models (Walter) Spring.
  • TTIC 31120: Statistical and Computational Learning Theory (Srebro) Spring

Applications

Note: Students must also take courses to satisfy their core degree requriements. For more detailed information see the CS, Stat and TTI course lists.

 

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