Description:

Pattern recognition deals with automated classification, identification, and/or characterizations of signals/data from various sources. The main objectives of this graduate module are to equip students with knowledge of common statistical pattern recognition (PR) algorithms and techniques. Upon completion of this module, students will be able to analyze a given pattern recognition problem, and determine which standard technique is applicable, or be able to modify existing algorithms to engineer new algorithms to solve the problem.

Please note, this website covers only the 1st half of the course.

Textbooks:

• "Pattern Recognition and Machine Learning" (PRML), by Christopher Bishop.
• "Pattern Classification" (PC), by Duda, Hart, Stork
• "Machine Learning: A Probabilistic Perspective" (MLPP), by Murphy

Instructor: Robby T. Tan

Teaching Schedule:

The following schedule will not be strictly followed.

Date	Topic	Lecture Note
August 17	1. INTRODUCTION + LEAST SQUARE REGRESSION Reading: PRML Chapter 1 (Introduction): Sect. 1.1 (Polynomial Curve Fitting) PC Chapter 1 (Introduction) Least Sqaures: wikipedia Matrix Calculus: wikipedia Overfitting: wikipedia Overdetermined systems: wikipedia Additional resources (optional): Introduction to Machine Learning: pdf | youtube Pseudoinverse: YouTube	Lecture note 1 Logistics
August 24	2. BAYESIAN INFERENCE Reading: PRML Chapter 1 (Introduction): Sect. 1.2 (Probability Theory), Sect. 1.3 (Model Selection) Introduction to Bayesian inference: video	Lecture note 2
August 31	3. MLE + MAP FOR REGRESSION Reading: PRML Chapter 1 (Introduction): Sect. 1.2.5 (Curve Fitting Re-Visited) Bayesian Inference: An Introduction to Principles and Practice in Machine Learning (Sect. 2.1, 2.3, 2.4): pdf Additional resources: MLE: YouTube MAP: YouTube	See Lecture note 2 Assignment 1 (CA1)
September 7	4. FULL BAYESIAN FOR REGRESSION Reading: PRML Chapter 3: Linear Models for Regression (Sect. 3.1 only) PRML Chapter 2: Sect. 2.3.1 to 2.3.6 (Gaussian Distribution) PRML Chapter 3: Sect. 3.3.1 (Parameter Distribution) Additional resources: Basis functions: YouTube 1 | YouTube 2 Spline functions: demo applet (use Firefox) Covariance matrix: wikipedia Geometric interpretation of covariance matrix: website	Lecture note 4
September 14	5. PREDICTIVE DISTRIBUTION (REGRESSSION) + INTRO TO CLASSIFICATION Reading: PRML Chapter 3: Sec. 3.1.3 (Sequential Learning), and Sec. 3.3.1 (Parameter Distribution) PRML Chapter 3: Sect. 3.3.2 (Predictive Distribution) PRML Chapter 4: Sect. 4.3.2 (logistic regression) and 4.3.3 (iterative reweighted least squares)	Lecture note 5
September 21	6. BAYESIAN INFERENCES FOR CLASSIFICATION Reading: PRML Chapter 4: Sect. 4.4 (Laplace Approximation) PRML Chapter 4: Sect. 4.5.1 (Bayesion Logistic Regression: Laplace Approximation) Bayesian Inference: An Introduction to Principles and Practice in Machine Learning (From Section 1 to Section 3 only): pdf	Lecture note 6
September 28	RECESS WEEK

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