導論：模式識別，特徵檢測與分類
復習：概率論，條件概率，貝葉斯法則
隨機向量，期望，相關，協方差
復習：線性代數，線性變換
決策論，ROC曲線，似然率測試
線性判別式，二次型判別式，Fisher判別式
充分統計量，處理迷失特徵或者雜訊特徵
基於範本的識別，特徵向量分析，特徵提取
訓練方法，最大似然估計和貝葉斯參數估計
線性判別式，感知器學習，梯度下降優化演算法，支撐向量機（SVM）
K最近鄰分類
非參數分類，密度估計，Parzen估計
非監督學習，聚類，向量量化，K均值
混合（Mixture）模型，最大期望（E－M）優化方法
隱馬爾可夫模型，Viterbi演算法，Baum-Welch演算法
線性動態系統，卡爾曼濾波和平滑處理
貝葉斯網路，獨立圖
決策樹，多層感知器
多個分類器的融合，「委員會機器」

• 初始信息
• 提供初始數據
• 一頁的計畫書（如果不使用標準的資料集合）
• 一頁的計畫書（如果使用標準的資料集合）
• 專案專題講演，提交線上講演
• 專案專題講演（第二堂課繼續）

25% 期中考核

10% 在講授課程（尤其是最後兩天）和復習課程中的表現和互動以及與指導教師在課堂外的交流。

所有作業必須在截至日當天的5:00pm前上交到助教的辦公室。也可在截至日當天將作業帶到教室提交。如果沒有按時提交，則該次作業成績為零分。但是最低的作業成績將不會計入最終課程成績。

作業的目的是幫助學習，並不是看你們能夠獲得多少分數。在研究所學習中的考試成績並不重要，更重要的是你們學到了什麼知識。因此，如果你們偶然中發現了在過去的課程材料中有類似的習題，不要去看他們的解答，而是獨立做答。你們可以向任課教師尋求幫助或者同學之間互相合作解答問題。協作是建立在公開討論的基礎上的，可以隨意交流想法，技術，甚至細節，但是要獨立撰寫你們的解答。這包括獨立完成Matlab程式，不得從他人的解答或者往年類似的習題的解答中抄襲。如果違反這一規定，該次作業的成績計為不合格（F），並且最終課程成績也可能記為不合格（F）。如果你們是合作完成一個專題，則需要遞交一個報告，並且在報告中注明各人負責哪些部分。期中考試是閉卷形式，但允許攜帶作弊紙條（譯者注：指學生可以攜帶一些紙，在上面總結一些公式）。

所有教師在任何時間都歡迎你們的意見。在向我們提供回饋意見時請不必拘束，在以往，我們就曾收到過很有幫助性的意見，這些意見使得我們改進我們的課程。我們希望最大化該課程對所有學生的價值，歡迎你們的回饋意見，正面意見或者負面意見均可。

希望所有學生都出席在最終兩堂課進行的所有的項目專題講演，這將是非常有教育意義的經歷，並有助於提高你們最終的課程成績。




MATLAB® 為 The MathWorks, Inc 的商標。

Intro to pattern recognition, feature detection, classification
Review of probability theory, conditional probability and Bayes rule
Random vectors, expectation, correlation, covariance
Review of linear algebra, linear transformations
Decision theory, ROC curves, Likelihood ratio test
Linear and quadratic discriminants, Fisher discriminant
Sufficient statistics, coping with missing or noisy features
Template-based recognition, eigenvector analysis, feature extraction
Training methods, Maximum likelihood and Bayesian parameter estimation
Linear discriminant/Perceptron learning, optimization by gradient descent, SVM
k-nearest-neighbor classification
Non-parametric classification, density estimation, Parzen estimation
Unsupervised learning, clustering, vector quantization, K-means
Mixture modeling, optimization by Expectation-Maximization
Hidden Markov models, Viterbi algorithm, Baum-Welch algorithm
Linear dynamical systems, Kalman filtering and smoothing
Bayesian networks, independence diagrams
Decision trees, Multi-layer Perceptrons
Combination of multiple classifiers "Committee Machines"

• Initial Information Available
• Data Available
• One Page Plan Due If Not Using Standard Data Set
• One Page Plan Due If Using Standard Data Set
• Project Presentations, Online Presentation Due
• Project Presentations Continued

25% Midterm

10% Your presence and interaction in lectures (especially the last two days), in recitation, and with the staff outside the classroom.

Assignments are due by 5:00 p.m. on the due date, in the TA's office. You are also free to bring them to class on the due date. If you are late, you will get a zero on the assignment. However, the lowest assignment grade will be dropped in computing the final grade

The goal of the assignments is to help you learn, not to see how many points you can get. Grades in graduate school do not matter very much: what you learn really does matter. Thus, if you stumble across old course material with similar-looking problems, please try not to look at their solutions, but rather work the problem(s) yourself. Please feel free to come to the staff for help, and also to collaborate on the problems and projects with each other. Collaboration should be at the "whiteboard" level: discuss ideas, techniques, even details - but write your answers independently. This includes writing Matlab code independently, and not copying code or solutions from each other or from similar problems from previous years. If you are caught violating this policy it will result in an automatic F for the assignment AND may result in an F for your grade for the class. If you team up on the final project, then you may submit one report which includes a jointly written and signed statement of who did what. The midterm will be closed-book, but we will allow a cheat sheet.

The staff welcomes your comments on the course at any time. Please feel free to send us comments -- in the past, we have obtained helpful remarks that allow us to make improvements mid-course. We want to maximize the value of this course for everyone and welcome your input, positive or negative.

All students are expected to attend all project presentations the last two days of class; these tend to be very educational experiences, and thus attendance these last two days will contribute to your final grade.




MATLAB® is a trademark of The MathWorks, Inc.