Pietro CORETTO | STATISTICAL LEARNING
Pietro CORETTO STATISTICAL LEARNING
cod. 0212800017
STATISTICAL LEARNING
| 0212800017 | |
| DEPARTMENT OF ECONOMICS AND STATISTICS | |
| EQF6 | |
| STATISTICS FOR BIG DATA | |
| 2021/2022 |
| OBBLIGATORIO | |
| YEAR OF COURSE 3 | |
| YEAR OF DIDACTIC SYSTEM 2018 | |
| AUTUMN SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| SECS-S/01 | 10 | 60 | LESSONS |
| Objectives | |
|---|---|
| KNOWLEDGE AND UNDERSTANDING THE COURSE INTRODUCES AUTOMATED STATISTICAL LEARNING METHODS (MACHINE LEARNING) WITH AN EMPHASIS TO APPLICATIONS TO MODERN MASSIVE DATA SETS (BIG DATA). AFTER COMPLETING THE COURSE THE STUDENT WILL BE ABLE TO DEVELOP AUTOMATIC DATA ANALYSIS PROCEDURES TO EXTRACT PATTERNS, TRENDS AND FORMULATE PREDICTIONS BASED ON THE USE OF HIGH-DIMENSIONAL AND COMPLEX DATABASES. IN PARTICULAR IT IS EXPECTED THAT A STUDENT ACQUIRE: -KNOWLEDGE OF MODERN ALGORITHMS FOR SUPERVISED LEARNING -KNOWLEDGE OF MODERN ALGORITHMS FOR UNSUPERVISED LEARNING -KNOWLEDGE OF MOST MODERN AND POPULAR SOFTWARE LIBRARIES -ABILITY TO VALIDATE AND INTERPRET SOLUTIONS PROVIDED BY AN ALGORITHM IN THE SPECIFIC CONTEXT APPLYING KNOWLEDGE AND UNDERSTANDING ON THE BASIS OF ACQUIRED KNOWLEDGE THE STUDENT WILL BE ABLE TO -UNDERSTAND THE TECHNICAL ASPECT UNDERLYING THE MAIN ALGORITHMS FOR STATISTICAL LEARNING -SELECT AND APPLY THE APPROPRIATE TOOL DEPENDING ON THE CONTEXT AND SPECIFIC PROBLEM TO BE SOLVED -ABILITY TO USE THE MAIN COMPUTATIONAL TOOLS FOR SOLVING RELEVANT PRACTICAL PROBLEMS OFTEN ARISING IN THE FIELD OF FINANCE AND ECONOMICS |
| Prerequisites | |
|---|---|
| ON THE BASIS OF ACQUIRED KNOWLEDGE THE STUDENT WILL BE ABLE TO -UNDERSTAND THE TECHNICAL ASPECT UNDERLYING THE MAIN ALGORITHMS FOR STATISTICAL LEARNING -SELECT AND APPLY THE APPROPRIATE TOOL DEPENDING ON THE CONTEXT AND SPECIFIC PROBLEM TO BE SOLVED -ABILITY TO USE THE MAIN COMPUTATIONAL TOOLS FOR SOLVING RELEVANT PRACTICAL PROBLEMS OFTEN ARISING IN THE FIELD OF FINANCE AND ECONOMICS |
| Contents | |
|---|---|
| THE COURSE WILL COVER THE FOLLOWING TOPICS - STATISICAL LEARNING AND PREDICTION - CLASSIFICATION AND OPTIMALITY UNDER 0-1 LOSS - LOGISTIC CLASSIFIER - GEMOETRY OF MULTIVARIATE DATA: DISTANCE, DISSIMILARITY, CORRELATION AND ELLIPTICALLY-SYMMETRIC SHAPES - LDA, QDA AND KNN CLASSIFIERS - PREDICTION ERROR AND BIAS-VARIANCE TRADE-OFF - CROSS-VALIDATION AND ERROR ESTIMATION - DATA COMPRESSION AND REDUCTION - PRINCIPAL COMPONENTS - CLUSTERING AND ALGORITHMS: K-MEANS, K-MEDOIDS AND HIERARCHICAL METHODS - CLUSTER SELECTION AND VALIDATION |
| Teaching Methods | |
|---|---|
| LECTURES, LAB CLASSES AND CASE STUDIES |
| Verification of learning | |
|---|---|
| THE FINAL EXAM CONSISTS OF A WRITTEN AND AN ORAL EXAM. BOTH PARTS WILL BE EVALUATED ON A NUMERICAL SCALE BETWEEN 1 AND 30. TO ACCESS THE ORAL PART A MINIMUM OF 18/30 IS REQUIRED FOR THE WRITTEN PART. DURING THE WRITTEN TEST (WHICH LAST ABOUT 2H) THE STUDENT WILL RECEIVE AN EXAM PAPER AND WILL BE ASKED TO ANSWER ABOUT 8 QUESTIONS (EACH WITH SCORES RANGING FROM 1 POINT TO 7 POINTS) ON THE ENTIRE PROGRAM OF THE COURSE, USING A DATASET PROVIDED DURING THE EXAM. THE ORAL EXAM (LASTING ABOUT 15 MINUTES) FOCUSES ON THE GENERAL KNOWLEDGE OF THE TOPICS TREATED DURING THE COURSE, THE ABILITY TO PRODUCE A CORRECT STATISTICAL ANALYSIS, THE ABILITY TO CORRECTLY COMMUNICATE THE RESULTS. THE FINAL MARK WILL REFLECT THE EFFECTIVINESS OF THE TOOLS EMPLOYED, OF THE THOROUGHNESS AND LUCIDITY OF ANSWERS. THE FINAL MARK, ON A SCALE BETWEEN 1 AND 30 WITH LAUDE, WILL CONSIDER BOTH THE PERFORMANCE ON THE WRITTEN AND ORAL PART. |
| Texts | |
|---|---|
| LECTURES NOTES OF THE INSTRUCTOR AND REFERENCES AVAILABLE ON-LINE SUGGESTED BY THE INSTRUCTOR JAMES, G., WITTEN, D., HASTIE, T., & TIBSHIRANI, R. (2013). AN INTRODUCTION TO STATISTICAL LEARNING (VOL. 112, PP. 3-7). NEW YORK: SPRINGER. |
| More Information | |
|---|---|
| ADDITIONAL INFORMATION WILL BE AVAILABLE ON THE WEB PAGE OF THE INSTRUCTOR. ATTENDANCY EVEN IF NOT COPULSORY IS STRONGLY ENCOURAGED. |
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