course_machine_learning

Public repository for lecture notes / labs, etc

Course Logistics

Schedule

• Semester: Fall 2026
• Instructor: Tao LIN
• Time and Location:
  • Theory: Tuesday 13:30 - 14:15, 14:20 - 15:05, 15:10 - 15:55, YunGu campus E13-103
  • Exercise: Thursday 08:00 - 09:35, YunGu campus E13-103
• Canvas

Grading

See course_info_sheet for more details.

• Assignments: 20%
• Project 1: 10%, individual, due Oct. 22 at 23:59
• Project 2 (Auto-Research Challenge): 30%, individual, due Dec. 24 at 23:59
• Final exam: 40%

Syllabus

Date	Session	Class Hour	Instructor	Theme / Topic	Teaching Activities (Lecture/Practical)
Sep. 01	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. 03	Foundations of Data Science		TA	Lab 1 (graded): mathematical foundations	Practical
Sep. 08	Lecture 2: Linear Models for Regression		Tao Lin	Linear regression; Cost functions; Introduction to optimization	Lecture
Sep. 10	Linear Models for Regression		TA	Lab 2: Introduction to Python, NumPy, and PyTorch; Lab 1 due; Project 1 release	Practical
Sep. 15	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. 17	Linear Models for Regression		TA	Lab 3	Practical
Sep. 22	Lecture 4: Generalization, and Model Selection		Tao Lin	Generalization; Bias-Variance decomposition; Double descent phenomenon; Model selection, and validation	Lecture
Sep. 24	Generalization, and Model Selection		TA	Lab 4 (graded)	Practical
Sep. 29	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. 08	Linear Models for Classification		TA	Lab 5; Lab 4 due	Practical
Oct. 13	Lecture 6: Generalized Linear Models		Tao Lin	Exponential family; Generalized linear models	Lecture
Oct. 15	Generalized Linear Models		TA	QA for Project 1	Practical
Oct. 20	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. 22	Generative Learning Algorithms		TA	QA for Project 1; Project 1 due; Project 2 release	Practical
Oct. 27	Lecture 8: Mixture Models, EM Algorithm		Tao Lin	Introduction to unsupervised Learning; Clustering; K-means; Gaussian Mixture Model (GMM); EM algorithm	Lecture
Oct. 29	Mixture Models, EM Algorithm		TA	Lab 8	Practical
Nov. 03	Lecture 9: Nonparametric Methods		Tao Lin	Parametric vs. nonparametric models; K-nearest neighbors; Decision trees; Bagging and random forest	Lecture
Nov. 05	Nonparametric Methods		TA	Lab 7	Practical
Nov. 10	Lecture 10: Kernel Methods, SVM		Tao Lin	Kernel methods; Support Vector Machine (SVM)	Lecture
Nov. 12	Kernel Methods, SVM		TA	Lab 6 (graded)	Practical
Nov. 17	Lecture 11: Neural Networks		Tao Lin	Neural Networks - basics, representation power; Neural Networks – back-propagation	Lecture
Nov. 19	Neural Networks		TA	Lab 9; Lab 6 due	Practical
Nov. 24	Lecture 12: Deep Neural Networks		Tao Lin	Deep Neural Networks – advanced architectures (CNN, RNN, Transformer, etc.)	Lecture
Nov. 26	Deep Neural Networks		TA	Lab 10 (graded)	Practical
Dec. 01	Lecture 13: Deep Neural Networks		Tao Lin	Deep Neural Networks – optimization	Lecture
Dec. 03	Deep Neural Networks		TA	Lab 11 (graded); Lab 10 due	Practical
Dec. 08	Lecture 14: Deep Neural Networks		Tao Lin	Beyond supervised learning: zero/few-shot learning	Lecture
Dec. 10	Deep Neural Networks		TA	Lab 11 due	Practical
Dec. 15	Lecture 15: Deep Neural Networks		Tao Lin	Self-supervised Learning; LLMs	Lecture
Dec. 17	Deep Neural Networks		TA	Lab 12; QA for Project 2	Practical
Dec. 22	Recitation		Tao Lin	Course recitation	Lecture
Dec. 24	Auto-Research Challenge		TA	QA for Project 2; Project 2 due	Practical