Organized by Rabindrik Psychotherapy Research Institute Trust (RPRIT), Registered Academic Trust by Indian Trust Act,1882, REGD.NO.150600103.
Registration form: https://docs.google.com/forms/d/118cgX8cIeJqB_AI2H_68CgASm0AFZyhfAXaFQEJ2r1U/edit
About the trust: It is a higher learning virtual research institute aiming at educating students about research for the welfare of the larger community through online and blended education. International students are welcome here. The trust regularly inviting applications for various courses. Both Internal and External Faculties are teaching here. The trust has more than 500 students.
Learning Outcomes: Machine learning in Psychology is important to provide real time feedback about psychological test scores. This will reduce psychological testing and counseling time. RPRIT developed some supervised and unsupervised machine learning techniques for the same. Students will learn those principles by the end of this course, will be proficient in using SPSS to perform machine learning, interpret and report the result, develop skills in visualizing and presenting findings.
Syllabus:
Unit 1: Introduction to machine learning: Objective: To provide students with a basic understanding of machine learning concepts and their relevance to psychology.
- 1.1 Definition, types of Machine Learning.
- 1.2 Key concepts and their Psychological Applications.
- 1.3. Differences between traditional statistics and machine learning.
- 2.1 Machine learning in Mental health and Personality: Mental health diagnostic and prediction , Personality assessment, Emotion detection.
- 2.2 Machine learning in Education, Guidance and Counseling: Personalized Learning, automated assessment, Job matching, emotional support, group out prevention.
- 2.3 Machine learning in Forensic psychology: Criminal profile analysis, Lie detection, predicting victimology, Criminal behavior prediction.
- 3.1 Variable Transformation: Recoding, Dummy Variable and Standardization
- 3.2 Splitting Data Sets: Training and Testing data, Handling missing data,Creating new variables.
- 3.3 Introduction to exploratory data analysis(EDA) and Data cleaning.
- 4.1 Fundamentals of Logistic Regression: difference between linear and logistic regression and applications of psychology.
- 4.2 Key concepts of Logistic Regression.
- 4.3 Logistic Regression Using SPSS.
- 5.1 Overview of unsupervised learning and applications, Mathematical Foundations: K-Means algorithm, Distance Metrics.
- 5.2 K-Means Clustering in SPSS: Setting up the data and running K-Means clustering, output interpretation.
- 5.3 Evaluating and interpreting K-Means clustering result.
- 6.1. Project proposal writing.
- 6.2. Project on Logistic Regression.
- 6.3. Project K-Means clustering
Qualification and Eligibility: Post graduation in Psychology and in Allied Sciences Related Fields. Basic knowledge of Psychology and Should have SPSS in laptop. Interested Candidates will collect data for learning.
- Duration: 3 months.
- Credit Points: 3(36 Hours)
- Registration Fees is non-refundable: Rs.5k and Rs.6k after 31st January. Instalment scheme is available (60% , 40%).
- Seats: 10.
- Day and Timings: The sessions will be held online via Zoom from 8 to 9 PM on Monday, Thursday, and Saturday, and in hybrid mode at Shriram Grand City, Grand One, near Konnagar Rail Station, Kolkata.
- Certificate :Participation certificate will be provided after course completion. After successful completion of the course, student could join the institute as per eligibility.
- Pedagogy: Flipped classroom model, students will be given learning material before the class, hands on experience in class, work at home for assignments, Lecture through zoom, dealing with real life problems, Google classroom, WhatsApp group. Resource persons will be external and internal faculties.
Important Dates:
- Registration Starts: 14/1/2025
- Registration Closes: 16/02/2025
- Class Starts: 20/02/2025
- Class Ends: 22/05/2025
Contact us:
Dr Rama Manna - Academic coordinator | M: +919903542602
Farha - Program Coordinator | M: +919998301691
Account Details: Account number: 920020072908427, IFSC Code:- UTIB0000236 , AXIS BANK, DUNLOP (KOLKATA). You can pay by Gpay. Payment in favor of Rabindrik Psychotherapy Research Institute Trust.
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Here’s a structured **8-week calendar** for the **Machine Learning in Psychology Research** course using the **Flipped Classroom Model** and **Regular Assignments**:
---
### **Week 1: Introduction to Machine Learning**
**Pre-Class (Self-Study & Video Lectures):**
- Watch videos on **Types of Machine Learning** (Supervised, Unsupervised, Reinforcement).
- Read an article on **Psychological Applications of ML**.
- Reflect on how ML differs from traditional statistics.
**Live Class Activities:**
- Discussion on how ML applies to Psychology.
- Case study: ML in **mental health diagnostics**.
- Group exercise: Identifying research areas where ML can be applied.
**Assignment:**
- Write a **500-word reflection** on the role of ML in psychology.
---
### **Week 2: Applications of Machine Learning**
**Pre-Class:**
- Read case studies on ML in **mental health, education, forensic psychology**.
- Watch an explainer video on **emotion detection and AI-based personality assessment**.
**Live Class Activities:**
- Discussion on **ethical concerns** in using ML in Psychology.
- Case study on **criminal profiling** using ML.
- Group work: Designing a simple ML-based career counseling tool.
**Assignment:**
- Research and present a short **case study** on ML in any psychology field.
---
### **Week 3: Data Preparation with SPSS**
**Pre-Class:**
- Read about **variable transformation** (dummy variables, standardization).
- Watch a tutorial on **splitting datasets** in SPSS.
**Live Class Activities:**
- Hands-on practice: **Recoding, handling missing data, and EDA in SPSS**.
- Small group task: Identifying **data cleaning issues** in psychological datasets.
**Assignment:**
- Perform **data cleaning on a given dataset** in SPSS and submit a report.
---
### **Week 4: Supervised Learning – Logistic Regression**
**Pre-Class:**
- Watch a tutorial on **logistic regression basics**.
- Read about **differences between linear and logistic regression**.
**Live Class Activities:**
- Hands-on **logistic regression in SPSS**.
- Discussion on how logistic regression can be used for **predicting mental health risks**.
**Assignment:**
- Analyze a dataset using **logistic regression** and interpret the output.
---
### **Week 5: Unsupervised Learning – K-Means Clustering**
**Pre-Class:**
- Read about **K-Means clustering and distance metrics**.
- Watch an SPSS tutorial on **setting up clustering analysis**.
**Live Class Activities:**
- Hands-on session on **running K-Means clustering in SPSS**.
- Group work: **Interpret real-world clustering results** from psychology studies.
**Assignment:**
- Perform **K-Means clustering** on a dataset and submit a report.
---
### **Week 6: Project Proposal Writing**
**Pre-Class:**
- Read an article on **how to write a research proposal**.
- Watch a video on **selecting an ML project topic in psychology**.
**Live Class Activities:**
- Brainstorming session on **project ideas**.
- Review and provide feedback on **proposal drafts**.
**Assignment:**
- Submit a **one-page project proposal**.
---
### **Week 7: Project Execution – Logistic Regression**
**Pre-Class:**
- Refine dataset and finalize research question.
**Live Class Activities:**
- Implement **Logistic Regression on project dataset**.
- Peer review: Discuss issues and refine models.
**Assignment:**
- Submit **preliminary results** from logistic regression analysis.
---
### **Week 8: Project Execution – K-Means Clustering & Final Submission**
**Pre-Class:**
- Refine dataset and **review feedback** from logistic regression.
**Live Class Activities:**
- Implement **K-Means Clustering on project dataset**.
- Group discussion: **Challenges and learnings**.
**Final Assignment:**
- Submit **final project report** (including Logistic Regression and K-Means Clustering).
- **Presentation** of key findings.
---
### **Assessment Structure**
1. **Participation & Assignments (30%)**
2. **Mid-course Quiz (10%)**
3. **Project Proposal (10%)**
4. **Project Report & Presentation (50%)**
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