course_machine_learning

Public repository for lecture notes / labs, etc

Course Logistics

Schedule

• Semester: Fall 2025
• Instructor: Tao LIN
• Time and Location:
  • Theory: Tuesday 13:30 - 14:15, 14:20 - 15:05, 15:10 - 15:55, YunGu campus E13-204
  • Exercise: Thursday 08:00 - 09:35, YunGu campus E13-204
• Canvas

Grading

See course_info_sheet for more details.

Syllabus

Date	Session	Class Hour	Instructor	Theme / Topic	Teaching Activities (Lecture/Practical)
Sep. 02	Lecture 0 & 1: Foundations of Data Science		Tao Lin	Course logistics; Introduction to ML (why ML, and why now); Linear algebra review; Probability review	Lecture
Sep. 04	Foundations of Data Science		TA	Lab 1 (graded): mathematical foundations	Practical
Sep. 09	Lecture 2: Linear Models for Regression		Tao Lin	Linear regression; Cost functions; Introduction to optimization	Lecture
Sep. 11	Linear Models for Regression		TA	Lab 2: Introduction to Python, NumPy, and PyTorch; Lab 1 due; Project 1 release	Practical
Sep. 16	Lecture 3: Linear Models for Regression		Tao Lin	Least squares; Probabilistic interpretation: Maximum Likelihood Estimation (MLE); Over- and under-fitting; Ridge regression; Lasso	Lecture
Sep. 18	Linear Models for Regression		TA	Lab 3	Practical
Sep. 23	Lecture 4: Generalization, and Model Selection		Tao Lin	Generalization; Bias-Variance decomposition; Double descent phenomenon; Model selection, and validation	Lecture
Sep. 25	Generalization, and Model Selection		TA	Lab 4 (graded)	Practical
Sep. 30	Lecture 5: Linear Models for Classification		Tao Lin	Classification; Logistic regression; Logistic regression and its optimization (MLE, Steepest descent, Newton's method, etc.)	Lecture
Oct. 09	Linear Models for Classification		TA	Lab 5; Lab 4 due	Practical
Oct. 14	Lecture 6: Generalized Linear Models		Tao Lin	Exponential family; Generalized linear models	Lecture
Oct. 16	Generalized Linear Models		TA	QA for Project 1;	Practical
Oct. 21	Lecture 7: Generative Learning Algorithms		Tao Lin	Discriminative vs. Generative learning algorithms; Gaussian Discriminant Analysis (GDA); GDA and Linear Discriminant Analysis (LDA); GDA and Naïve Bayes; GDA vs. Logistic regression	Lecture
Oct. 23	Generative Learning Algorithms		TA	QA for Project 1; Project 1 due; Project 2 release	Practical
Oct. 28	Lecture 8: Mixture Models, EM Algorithm		Tao Lin	Introduction to unsupervised Learning; Clustering; K-means; Gaussian Mixture Model (GMM); EM algorithm	Lecture
Oct. 30	Mixture Models, EM Algorithm		TA	Lab 8	Practical
Nov. 04	Lecture 9: Nonparametric Methods		Tao Lin	Parametric vs. nonparametric models; K-nearest neighbors; Decision trees; Bagging and random forest	Lecture
Nov. 06	Nonparametric Methods		TA	Lab 6 (graded)	Practical
Nov. 11	Lecture 10: Kernel Methods, SVM		Tao Lin	Kernel methods; Support Vector Machine (SVM)	Lecture
Nov. 13	Kernel Methods, SVM		TA	Lab 7; Lab 6 due;	Practical
Nov. 18	Lecture 11: Neural Networks		Tao Lin	Neural Networks - basics, representation power; Neural Networks – back-propagation	Lecture
Nov. 20	Neural Networks		TA	Lab 9	Practical
Nov. 25	Lecture 12: Deep Neural Networks		Tao Lin	Deep Neural Networks – advanced architectures (CNN, RNN, Transformer, etc.)	Lecture
Nov. 27	Deep Neural Networks		TA	Lab 10 (graded)	Practical
Dec. 02	Lecture 13: Deep Neural Networks		Tao Lin	Deep Neural Networks – optimization	Lecture
Dec. 04	Deep Neural Networks		TA	Lab 11 (graded)	Practical
Dec. 09	Lecture 14: Deep Neural Networks		Tao Lin	Beyond supervised learning: - zero/few shot learning	Lecture
Dec. 11	Deep Neural Networks		TA	Lab 11 (due)	Practical
Dec. 16	Lecture 15: Deep Neural Networks		Tao Lin	Self-supervised Learning; LLMs	Lecture
Dec. 18	Deep Neural Networks		TA	Lab 12; QA for Project 2	Practical
Dec. 23	Recitation		Tao Lin	Course recitation	Lecture
Dec. 25			TA	QA for Project 2; Project 2 due	Practical