Welcome to Machine learning course. What is machine learning? It is concerned with the question of how to write computer programs that automatically improve with experience. Over the very recent years, the field has expanded so much in every direction of our daily lives that we mostly are unaware of its existence. But, as a concerned citizen of the world, we are going to know the nuts and bolts of machine learning.

The course is based on fundamental knowledge of computer science principles and techniques, probability and statistics, calculus, and the theory and application of linear algebra. The course provides a broad introduction to pattern recognition from given data and how it can relate to machine learning. Topics include supervised learning, unsupervised learning, semi-supervised learning, neural network, and reinforcement learning algorithms. The course will also discuss recent applications of these machine learning concepts in solving real-world problems.

Course objectives

By the end of the course you are expected to gain the following skills:

1. Develop an understanding on how to extract patterns from data.
2. Develop an understanding on a wide variety of machine learning algorithms – how the algorithms work, and their practical usages.
3. Understand different types of optimization techniques, which are heavily utilized in many of the learning algorithms.
4. Capable of discussing pros and cons of the learning algorithms.
5. Be able to implement the covered algorithms in class by themselves using Python programming language.
6. Apply the algorithms to solve real world problems.
7. Understand and apply the principles of responsible AI system design: fairness, inclusiveness, transparency, reliability and safety, privacy and security, accountability, limits of capabilities.

Prerequisites

1. MATH-3195 (Linear algebra and differential equations) or equivalent,
2. CSCI-3412 (Algorithms) or equivalent.

Recommended Textbooks

1. Bishop, C. M. (2006). Pattern recognition and machine learning. We will call it the PRML book. Download PDF free at ]https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book](https://www.microsoft.com/en-us/research/people/cmbishop/#!prml-book). Springer.
2. Géron, A. (2025). Hands-On Machine Learning with Scikit-Learn and PyTorch. We will call it the Hands-on-ML book. Book website at https://www.oreilly.com/library/view/hands-on-machine-learning/9798341607972/. O’Reilly.
3. James, G., D. Witten, T. Hastie, et al. (2023). An introduction to statistical learning with applications in Python. We will call it the ISLPy book. Download PDF free at https://www.statlearning.com. Springer.

Homeworks

• Hw5 The problem here, Dataset here
• HW-extra credit The problem here

Schedule

Week 1 [1/20, 1/22]

Week 2 [1/27, 1/29]

Week 3 [2/3, 2/5]

Week 4 [2/10, 2/12]

Week 5 [2/17, 2/19]

Week 6 [2/24, 2/26]

Week 7 [3/3, 3/5]

Week 8 [3/10, 3/12]

Week 9 [3/17, 3/19]

• 3/17 Midterm So, let’s ignore this class.
• 3/19 Unsupervised Learning
  • Introduction to clustering slides
  • The k-means clustering slides
  • The Hierarchical clustering slides
  • Evaluating clustering results slides
  • Silhouette Coefficient to evaluate clustering results ipynb

Week 10 [3/24, 3/26]

• 3/24: Continuing unsupervised learning from week 9
• 3/26: Reinforcement Learning
  • Markov Chain (MC), Markov Reward Process (MRP)slides
    • Supporting codes zip

Week 11 [3/31, 4/2]

• 3/31: Markov Decision Process (MDP)
  • (MDP) slides
• 4/2: Model based reinforcement Learning algorithm
  • Slides slides

Week 12 and 13 [4/7, 4/9, 4/14, 4/16]

• Working with Non gymnasium environment codes

Week 14 [4/21, 4/23]

• 4/21 + 4/23 + 4/28: Principal Component analysis (PCA)
  • On principal Component analysis slides 1
  • PCA – a detailed introduction slides 2
  • PCA codes and dataset code/data

Week 15 [4/28, 4/30]

• 4/30: Ensemble Learning
  • Ensemble Learning slides
  • Ensemble learning example codes codes
  • The math behind why the misclassification rate of a majority voting strategy is a lot smaller than the individual base classifiers? ipynb
  • Intuition and thorough walkthrough the Adaboost algorithm slides

Week 16 [5/5, 5/7]

• 5/5: Responsible Machine Learning (Ethics and Bias in AI)
  • 00– Introduction to Responsible Machine Learning slides
    • A lecture by Prof. Rich Caruana on “Intelligible Models for Healthcare: Predicting Pneumonia Risk and Hospital 30-day ReadmissionYoutube Link <24min
  • 01– Types of Bias and bias metrics slides
  • Solved activities around measuring bias in model predictions and even on the original dataset pptx
  • A demo on a full bias audit code/notebook
• 5/7: Introduction to Artificial Neural Networks
  • Play with neural networks to solve various tasks at playground.tensorflow.org
  • Some backgrounds to understand ANN Code/Notebook/slides

Week 17 [5/12 Tuesday]

1. Reserved for the final exam (NORTH-1207, Date: 5/12/2026 at 9:30am (2 hours))