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10-701: Introduction to Machine Learning (PhD)

Category Difficulty (Out of 5)
Homeworks (Written) 5
Homeworks (Programming) 4
Project 4
Exams 5

The course 10-701 is a PhD level course in the Machine Learning Department at Carnegie Mellon University. The course is good for those who want to understand Machine Learning with a focus on theoretical aspects and foundations of it. This course also includes programming assignments and a project, however, students should note that the course has strong focus on theoretical foundations and suits PhD students who want to contribute to the Machine Learning field. The topics covered in this course are listed below:

Topics Covered

  1. Foundations of ML (Three axes of ML: Data, Algorithms, and Tasks, MLE, Bayesian Estimation, MAP, Decision Theory)
  2. Non-Parametric Models: K-Nearest Neighbors, Kernel Regression
  3. Linear Regression
  4. Regularized, Polynomial, Logistic Regression
  5. Decision Trees
  6. Naive Bayes, Generative vs Discriminative
  7. Neural Networks and Deep Learning
  8. Support Vector Machines
  9. Boosting, Surrogate Losses, Ensemble Methods
  10. Clustering, KMeans
  11. Graphical Models (Bayesian Networks)
  12. Sequence Models: HMMs, State Space Models, other time series models
  13. Generalization, Model Selection
  14. Learning Theory
  15. Representation Learning: Feature Transformation, Random Features, PCA, ICA
  16. Reinforcement Learning

Class Structure

  1. Lectures on the above topics mentioned
    1. Importance & Significance
    2. Theoretical Derivations
    3. Algorithms
  2. Industry Lecture
  3. Homeworks
    1. Written
    2. Programming
  4. Project
    1. Programming - using large real-world dataset

Homeworks and Project

The homework component of the course is divided into written and programming parts. The written component is usually the heaviest part in the homeworks which involves proofs, derivations, and other concepts taught in the class. Students are expected to know the concepts of Linear Algebra. The programming parts require you to develope algorithms learned in class and being proficient in Python language is important.

The project lets you learn working with real-world large datasets and apply different models and techniques learned in class for solving real-world problems. Through the project, you will also explore cloud computing platform AWS services.

Exams

The course involves two exams mainly during the mid-term and end-term weeks. Make sure to prepare as early as possible by going through the lectures, homeworks, and practicing previous years exams. The questions are similar to the homeworks written components which involve proofs and derivations.

Tips and Tricks to do Well

  • Definitely attend lectures and recitations. Can also review recordings later on your time.
  • Make sure to review your knowledge on Linear Algebra.
  • Refer to the external resources pointed out during the lectures.
  • Start Homeworks and prepare for the exam very early.
  • Follow-up on Piazza for any clarifications or doubts. One can also attend OH for any help.