Course Calendar

Deliverable with Deadlines

Tutorial Schedule

• Event
  Date
  Description
  Description
• Session
  01/06/2026 17:00
  Tuesday
  First Lecture
• Lecture
  01/06/2026
  Tuesday
  Lecture 0: Course Overview and Logistics

  Lecture Notes:

  • Lecture 0
• Lecture
  01/06/2026
  Tuesday
  Lecture 1: What is ML? | Clustering by K-means

  Lecture Notes:

  • Lecture 1

  Further Reads:

  • Introduction to ML, Chapter 1 - Sections 1.1 and 1.3 of [B]
  • Different Domains of ML, Chapter 1, Chapter 2 - Sections 2.1 and 2.2, Chapter 14 - Sections 14.1 and 14.2 of [HTF]
  • Clustering, Chapter 20 of [MK]
• Lecture
  01/08/2026
  Thursday
  Lecture 2: Deterministic and Probabilistic Clustering

  Lecture Notes:

  • Lecture 2

  Further Reads:

  • Clustering, Chapter 20 of [MK]
• Lecture
  01/13/2026
  Tuesday
  Lecture 3 - Part I: Density Learning and Maximum Likelihood

  Lecture Notes:

  • Lecture 3

  Further Reads:

  • Maximum Likelihood, Chapter 8 of [Probabilistic ML Book]
  • Maximum Likelihood and Clustering, Chapter 22 of [MK]
  • More on Probabilistic Modeling, Chapter 23 of [MK]
• Lecture
  01/13/2026
  Tuesday
  Lecture 3 - Part II: Gaussian Maximum Likelihood

  Lecture Notes:

  • Lecture 3

  Further Reads:

  • Gaussian Max-Likelihood, Chapter 2 - Sections 2.1 and 2.3 of [B]
• Assignment
  01/15/2026
  Thursday
  Assignment #1 - Clustering and Maximum Likelihood released!
  [Assignment #1 - Clustering and Maximum Likelihood]
• Lecture
  01/20/2026
  Tuesday
  Lecture 4 - Part I: Dimensionality Reduction and PCA

  Lecture Notes:

  • Lecture 4

  Further Reads:

  • PCA, Chapter 12 of [B]
  • PCA, Chapter 14 of [HTF]
  • Latent Space, Chapter 34 of [MK]
• Lecture
  01/20/2026
  Tuesday
  Lecture 4 - Part II: PCA as Maximal Representation Variance

  Lecture Notes:

  • Lecture 4

  Further Reads:

  • PCA, Chapter 12 of [B]
  • PCA, Chapter 14 of [HTF]
  • Latent Space, Chapter 34 of [MK]
• Lecture
  01/22/2026
  Thursday
  Lecture 5: Deriving PCA and Its Applications

  Lecture Notes:

  • Lecture 5

  Further Reads:

  • Probabilistic PCA, Paper Probabilistic Principal Component Analysis written by Michael E. Tipping and Christopher M. Bishop in 1999 giving the statistical viewpoint on PCA
  • Eigenface, Paper Eigenfaces for Recognition written by Matthew Turk and Alex Pentland in 1991 on Using PCA for Face Recognition
  • Recommendation System, Paper Matrix Factorization Techniques for Recommender Systems written by Yehuda Koren et al. in 2009 On Netflix Prize Winner Project
• Lecture
  01/27/2026
  Tuesday
  Lecture 6 - Part I: Supervised Learning - Risk Minimization vs Maximum Likelihood

  Lecture Notes:

  • Lecture 6

  Further Reads:

  • Overview on Risk Minimization: Paper An overview of statistical learning theory published by Vladimir N. Vapnik as an overview of his life-going developments in ML in the IEEE Transactions on Neural Networks in 1999
• Lecture
  01/27/2026
  Tuesday
  Lecture 6 - Part II: Linear Regression

  Lecture Notes:

  • Lecture 6

  Further Reads:

  • Linear Regression, Chapter 3 of [B]
  • Linear Regression, Chapter 3 of [HTF]
  • Maximum Likelihood View on Regression, Chapters 11 and 12 of [Probabilistic ML Book]
• Assignment
  01/29/2026
  Thursday
  Assignment #2 - PCA and Linear Regression released!
  [Assignment #2 - PCA and Linear Regression]
• Lecture
  01/29/2026
  Thursday
  Lecture 7: Logistic Regression

  Lecture Notes:

  • Lecture 7

  Further Reads:

  • Logistic Regression, Chapter 4 of [B]
  • Linear Classification, Chapter 4 of [HTF]
  • Maximum Likelihood View on Classification, Chapter 10 of [Probabilistic ML Book]
• Due
  01/29/2026 23:59
  Thursday
  Assignment #1 due
• Lecture
  02/03/2026
  Tuesday
  Lecture 8 - Part I: Classification with Confidence

  Lecture Notes:

  • Lecture 8

  Further Reads:

  • SVC, Chapter 12, Sections 12.1 and 12.2 of [HTF]
• Lecture
  02/03/2026
  Tuesday
  Lecture 8 - Part II: Function Optimization - Review

  Lecture Notes:

  • Lecture 8

  Further Reads:

  • Constrained Optimization, Chapter 5 of Convex Optimization
• Lecture
  02/05/2026
  Thursday
  Lecture 9 - Support Vector Classification

  Lecture Notes:

  • Lecture 9

  Further Reads:

  • SVC, Chapter 12, Sections 12.1 and 12.2 of [HTF]
  • SVNets Paper Support Vector Networks published by Cortes and Vapnik in 1995
  • Tutorials on SVM Survey A Tutorial on Support Vector Machines for Pattern Recognition published by Burges in 1998
• Lecture
  02/10/2026
  Tuesday
  Lecture 10 - Support Vector Machine

  Lecture Notes:

  • Lecture 10

  Further Reads:

  • Tutorials on SVM Survey A Tutorial on Support Vector Machines for Pattern Recognition published by Burges in 1998
• Assignment
  02/12/2026
  Thursday
  Assignment #3 - SVM released!
  [Assignment #3 - SVM]
• Lecture
  02/12/2026
  Thursday
  Lecture 11 - Generalization and Gradient Descent

  Lecture Notes:

  • Lecture 11

  Further Reads:

  • Model Assessment and Selection , Chapter 7 of [HTF]
  • Gradient Descent, Chapter 10: Section 10.10 of [HTF]
• Due
  02/12/2026 23:59
  Thursday
  Assignment #2 due
• Assignment
  02/24/2026
  Tuesday
  Project Proposal released!
  [Project Proposal]
• Exam
  02/24/2026 17:00
  Tuesday
  Midterm

  Topics:

  • The exam is 3 hours long
  • No programming questions
  • Starts at 5:00 PM
• Lecture
  02/26/2026
  Thursday
  Lecture 12 - Representation by a Network of Neurons

  Lecture Notes:

  • Lecture 12

  Further Reads:

  • NNs, Chapter 5, Section 5.1 of [B]
  • Intro to NNs, Chapter 11, Sections 11.1 and 11.3 of [HTF]
• Lecture
  03/03/2026
  Tuesday
  Lecture 13 - Expressive Power of Neural Networks

  Lecture Notes:

  • Lecture 13

  Further Reads:

  • Expressiveness of NNs: Paper Multilayer feedforward networks are universal approximators published by Hornik et al in 1989
  • Universal Approximation: Paper Approximation by superpositions of a sigmoidal function published in Mathematics of Control, Signals and Systems by George V. Cybenko in 1989
• Due
  03/03/2026 23:59
  Tuesday
  Assignment #3 due
• Assignment
  03/04/2026
  Wednesday
  Assignment #4 - MLP released!
  [Assignment #4 - MLP]
• Lecture
  03/05/2026
  Thursday
  Lecture 14 - Learning via Neural Networks

  Lecture Notes:

  • Lecture 14

  Further Reads:

  • Training NNs, Chapter 5, Section 5.2 of [B]
  • Fitting NNs, Chapter 11, Section 11.4 of [HTF]
  • Training NNs, Chapters 13, Section 13.4 of [Probabilistic ML Book]
• Lecture
  03/10/2026
  Tuesday
  Lecture 15 - Training Neural Networks

  Lecture Notes:

  • Lecture 15

  Further Reads:

  • Backpropagation, Chapter 5, Section 5.3 of [B]
  • Training NNs, Chapters 13, Sections 13.1 to 13.4 of [Probabilistic ML Book]
• Lecture
  03/12/2026
  Thursday
  Lecture 16 - Generalization

  Lecture Notes:

  • Lecture 16

  Further Reads:

  • Model Assessment and Selection , Chapter 7 of [HTF]
• Due
  03/13/2026 23:59
  Friday
  Proposal due
• Lecture
  03/17/2026
  Tuesday
  Lecture 17 - Part I: Regularization

  Lecture Notes:

  • Lecture 17

  Further Reads:

  • Regularization, Chapter 13, Section 13.5 of [Probabilistic ML Book]
  • Regularization, Chapter 6 of [Deep Learning]
• Lecture
  03/17/2026
  Tuesday
  Lecture 17 - Part II: Convolutional Nets I

  Lecture Notes:

  • Lecture 17

  Further Reads:

  • CNNs, Chapter 14 of [Probabilistic ML Book]
  • CNNs, Chapter 9 of [Deep Learning]
• Due
  03/18/2026 23:59
  Wednesday
  Assignment #4 due
• Assignment
  03/19/2026
  Thursday
  Assignment #5 - CNNs released!
  [Assignment #5 - CNNs]
• Lecture
  03/19/2026
  Thursday
  Lecture 18 - Convolutional Nets II and Sequence Models

  Lecture Notes:

  • Lecture 18

  Further Reads:

  • CNNs, Chapter 9 of [Deep Learning]
  • RNNs, Chapter 10 of [Deep Learning]
• Lecture
  03/24/2026
  Tuesday
  Lecture 19 - Introduction to Sequence Processing

  Lecture Notes:

  • Lecture 19

  Further Reads:

  • RNNs, Chapter 10 of [Deep Learning]
  • Basic LM Paper A Neural Probabilistic Language Model published in 2003 by Y. Bengio developing frist really-functioning LM
• Lecture
  03/26/2026
  Thursday
  Lecture 20 - Introduction to Generative Modeling

  Lecture Notes:

  • Lecture 20

  Further Reads:

  • Generative Modeling, Chapter 20 of [Deep Learning]
• Lecture
  03/31/2026
  Tuesday
  Lecture 20 - Introduction to Reinforcement Learning

  Lecture Notes:

  • Lecture 21

  Further Reads:

  • Reinforcement Learning: An Introduction by Sutton and Barto
  • Spinning Up in Deep RL by OpenAI
• Due
  04/02/2026 23:59
  Thursday
  Assignment #5 due