Algorithms for Machine Learning

CAP 4611 (Fall 2025)

Department of Computer Science
University of Central Florida
Orlando, FL, USA

Instructor and TA information

Instructor: Ali Siahkoohi

• Email: alisk@ucf.edu
• Office hours: Tuesdays at 3–4pm

TAs:

• Priyank Pathak
  • Email: priyank@ucf.edu
  • Office hours: Mondays, Wednesdays, and Fridays 1–2pm
• Xin Liang
  • Email: xin.liang@ucf.edu
  • Office hours: Tuesdays, Thursdays, and Fridays 10:30–11:30am
• Rafael Montalvo (grader)
  • Email: montalvorafael@ucf.edu

See webcourses for the office hour location.

Description

We introduce basic principles and techniques in the field of machine learning. These are some of the key tools behind the emerging field of data science. These techniques now run behind the scenes to discover patterns and make predictions in various applications in our daily lives. We focus on many of the core machine learning technologies, with motivating applications from a variety of disciplines.

The material for this course—including slides, homework assignments, and the overall website structure—is adapted from Mark Schmidt’s CPSC 340: Machine Learning and Data Mining course at the University of British Columbia, used with his permission. We are grateful for his contribution in providing foundational material that helped shape this course.

Textbooks

There is no required textbook for the class. A introductory book that covers many (but not all) the topics we will discuss is the Artificial Intelligence book of Rusell and Norvig (AI:AMA) or the Artificial Intelligence book of Poole and Mackworth. More advanced books include The Elements of Statistical Learning (ESL) by Hastie et al., Murphy’s Machine Learning: A Probabilistic Perspective (ML:APP) which can be accessed through the library, and Bishop’s Pattern Recognition and Machine Learning (PRML).

Grading

• Assignments 30%, Midterm 20%, Final 50%.
• There will be 2% extra credit for submitting teaching evaluation, if more than 80% of students submitted the evaluation.

Schedule

Lecture slides will be posted on the course website before each lecture. The instructor reserves the right to alter this schedule as needed.

Dates	Lecture Topics	Resources	Assignments/Notes
Aug 19, Aug 21	Motivation and Syllabus Exploratory Data Analysis Decision Trees	What is Machine Learning? Machine Learning Rise of the Machines Talking Machine Episode 1 Mathematics for Machine Learning Gotta Catch’em all Why Not to Trust Statistics Visualization Types Google Chart Gallery Other Tools A Visual Introduction to Machine Learning Decision Trees Entropy What is Big O Notation? AI:AMA 19.2-3, ESL: 9.2, ML:APP 16.2	Assignment 1 a1.zip Assignment instructions (updated) Big-O Notes
Aug 26, Aug 28	Fundamentals of Learning Probability Slides	7 Steps of Machine Learning IID Cross-validation Bias-variance	Assignment 1 due: Aug 29 Course Notation Guide Probability Notes
Sep 2, Sep 4	Probabilistic Classifiers Non-Parametric Models Ensemble Methods	No Free Lunch AI:AMA 19.4-5, ESL 7.1-7.4, 7.10, ML:APP 1.4, 6.5 Conditional probability (demo) Naive Bayes Probabilities and Battleship AI:AMA 12.6, ESL 4.3, ML:APP 2.2, 3.5, 4.1-4.2 K-nearest neighbours Decision Theory for Darts Norms AI:AMA 19.7, ESL 13.3, ML:APP 1.4 Ensemble Methods Random Forests Empirical Study Kinect AI:AMA 19.8, ESL: 7.11, 8.2, 15, 16.3, ML:APP 6.2.1, 16.2.5, 16.6	Assignment 2 a2.zip
Sep 9, Sep 11	Clustering More Clustering Outlier Detection	Clustering K-means clustering (demo) K-Means++ (demo) IDM 8.1-8.2, ESL: 14.3 DBSCAN (video, demo) Hierarchical Clustering Phylogenetic Trees IDM 8.4 Empirical Study IDM 8.3, ESL 14.3.12, ML:APP 25.5	Linear Algebra Notes Linear/Quadratic Gradients
Sep 16, Sep 18	Linear Regression Nonlinear Regression Gradient Descent	Linear Regression (demo, 2D data, 2D video) Least Squares Essence of Calculus Partial Derivative Gradient ESL 3.1-2, ML:APP 7.1-3, AI:AMA 19.6 Why should one learn machine learning from scratch? Essence of Linear Algebra Matrix Differentiation Fluid Simulation (video) ESL 5.1, 6.3 Gradient Descent Convex Functions	Assignment 2 due: Sep 19
Sep 23, Sep 25	Robust Regression Feature Selection Regularization	ML:APP 7.4 Genome-Wide Association Studies AIC BIC ESL 3.3, 7.5-7 ESL 3.4., ML:APP 7.5, AI:AMA 19.4	Assignment 3 a3.zip
Sep 30, Oct 2	More Regularization Linear Classifiers More Linear Classifiers	RBF video RBF and Regularization video ESL 6.7, ML:APP 13.3-4 Perceptron ESL 4.5, ML:APP 8.5 Support Vector Machines ESL 4.4, 12.1-2, ML:APP 8.1-3, 9.5 14.5, AI:AMA 19.6	Assignment 3 due: Oct 3
Oct 7, Oct 9	Feature Engineering Kernel Trick Stochastic Gradient Descent	Gmail Priority Inbox ESL 12.3, ML:APP 14.1-4 Stochastic Gradient Descent Theory and Practice ML:APP 8.5	Assignment 4 a4.zip
Oct 14, Oct 16	Boosting	AdaBoost (video) XGBoost (video) ML:APP 16.4	Midterm - Oct 16
Oct 21, Oct 23	MLE and MAP Principal Component Analysis	Maximum Likelihood Estimation ML:APP 9.3-4 Principal Component Analysis ESL 14.5, IDM B.1, ML:APP 12.2	Assignment 4 due: Oct 24 Max and Argmax Notes
Oct 28, Oct 30	More PCA Beyond PCA	Making Sense of PCA SVD Eigenfaces Non-Negative Matrix Factorization (original - access from UCF) Recommender Systems Netflix Prize ESL 14.6, ML: APP 13.8	Assignment 5 a5.zip
Nov 4, Nov 6	Multi-Dimensional Scaling Neural Networks Over-Parameterization	Nonlinear Dimensionality Reduction t-SNE demo UMAP demo ESL 14.8-9, IDM B.2 Google Video What is a Neural Network? Interactive Guide ML:APP 16.5, ESL 11.1-4, AI:AMA 21.1	Assignment 5 due: Nov 7
Nov 11, Nov 13	Deep Neural Networks	Fortune Article Deep Learning References Alchemy ML:APP 28.3, ESL 11.5, AI:AMA 21.2 and 21.4-5	Assignment 6 a6.zip Veterans Day - No Class Tuesday
Nov 18, Nov 20	Convolutions Convolutional Neural Networks Autoencoders Recurrent Neural Networks LSTMs and Transformers	But what is a convolution? Convolutional Neural Networks ML:APP 28.4, ESL 11.7, AI:AMA 21.3 AI:AMA 21.6-8	Assignment 6 due: Nov 21
Nov 25, Nov 27	What do we Learn?		Thanksgiving - No Class Thursday
Dec 2	Final Exam

Related Courses

• Machine Learning and Data Mining (UBC)
• Introduction to Machine Learning (Alberta)
• Practical Machine Learning (Berkeley)
• Machine Learning (MIT)
• Machine Learning (CMU)
• Course in Machine Learning (Maryland)
• Data Mining (CMU)